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Questions about handing the former Haley Pike Landfill to a private solar developer have been simmering for months. “This came up back in the fall, and in November council members expressed some concerns around transparency as this was the sole respondent to a city RFP (request for proposal) for the Haley Pike Landfill,” CivicLex’s Richard Young explained Monday. That sole respondent was Edelen Renewables, which would be charged $85 per acre under the proposed deal — substantially less than the $550 to $850 estimate first floated in a 2024 study, according to the Lexington Herald-Leader. Richard Dugas with the city’s environmental quality and public works department, said the lower per acre charges were the result of a more in-depth study by Edelen Renewables. “Now that Edelen Renewables has actually looked at the actual land and said this is what it’s going to cost them, and they financially modeled that out, the projected revenue is a lot less than our desktop study came out,” he said. Company head Adam Edelen pointed to unique constraints on the project. “We have a very difficult project to build on here. This is a dump. It is a landfill. It has legacy environmental liabilities that we have to make sure are protected,” he told the council. But concerns persisted about the value of the land on council, prompting city leaders to press pause on the lease, putting the future of the large solar panel installation in limbo for now.
The Sandals Foundation has begun installing solar energy systems at three western Jamaica schools as part of a broader effort to strengthen climate resilience in the education sector following Hurricane Melissa. (function () { var cb = Date.now(); var url = “https://ad.doubleclick.net/ddm/trackimp/N46002.2579645CARIBBEANNATIONALW/B31194710.438827226;dc_trk_aid=631925257;dc_trk_cid=249092787;ord=%5Btimestamp%5D;dc_dbm_token=${DC_DBM_TOKEN};dc_lat=;dc_rdid=;tag_for_child_directed_treatment=;tfua=;gdpr=${GDPR};gdpr_consent=${GDPR_CONSENT_755};ltd=;dc_tdv=1?””.replace(“[timestamp]”, cb); var img = new Image(1,1); img.src = url; })(); In a release dated Feb. 12, 2026, the organization said teachers and students across western Jamaica continue to adapt to adjusted learning environments in the aftermath of the storm. The foundation has invested approximately JMD $4 million to install solar systems at Cove Primary School in Hanover, Kings Primary and Infant School, and Culloden Infant School in Westmoreland. The installations represent the first phase of a strategic effort aimed at strengthening the energy resilience of schools across the island and meeting both immediate and long-term operational needs. “One of the factors driving our restoration efforts in schools is ensuring the implementation of durable and sustainable methods and technologies that provides a great level of flexibility,” said Heidi Clarke, executive director of the Sandals Foundation. While sections of the schools remain partially covered with tarpaulin for temporary roofing, the renewable energy infrastructure marks the start of what officials describe as a larger investment in rebuilding and modernization. “We’re taking things on a phased basis. The current installation has allowed schools to immediately meet some key operation and administrative needs. As we rebuild more permanent structures, our goal is to expand the solar energy system – allowing more operations of the school to be powered by the sun,” Clarke added. At Kings Primary and Infant School, Principal Marcia Tatham-Miller said the installation has already had a significant impact on daily operations. “The installation of solar panels at our schools has been transformative. They allow us to maintain lighting, operate essential equipment and continue instruction even during power outages. It makes us feel hopeful and empowered,” she said. The Whitehouse community has been without electricity for about three and a half months since the category five hurricane. At Culloden Infant School, Principal Michelle Whittingham said the solar system has enabled administrators to meet a range of student needs. “The solar has been a tremendous help. The school cook can now purchase meat and store for school lunches. The children missed and can now enjoy their ice cream treats because we are able to operate our refrigerator. Textbooks that were damaged in the storm are now supplemented with photocopy services. Teachers have been able to use their computers to aid in learning instructions, recharge their devices, and the air conditioning system that came with our new modular classrooms (also provided by the Sandals Foundation), will be powered during warm days,” Whittingham said. In Hanover, roof damage from the storm destroyed solar panels previously installed by the foundation at Cove Primary School. Principal Patrice Campbell said the replacement systems highlight the importance of renewable energy in school infrastructure. “Because hurricanes and severe weather events are becoming more frequent, schools must be built to withstand the future, not the past. Renewable energy reduces operational costs and allow more funds to go towards learning resources and student development. In addition, climate-smart technology ensures learning continues in emergencies. Sustainable school rebuild empowers schools to be safe protecting both students and staff,” Campbell said> P.O. Box 551712 Davie, Fl 33355
Published 10:00 pm Thursday, February 12, 2026 By BERIT THORSON | East Oregonian MISSION — When the sun beats down on Coyote Business Park during an Eastern Oregon summer, from now on, some of that sunlight will be transformed into electricity. The Department of Economic and Community Development with the Confederated Tribes of the Umatilla Indian Reservation finished a solar array project at the center for the Food Distribution Program on Indian Reservations. The project is part of tribal efforts to increase renewable energy use. Patrick Mills, a certified energy manager and project management professional with the department, said while the array took about nine months to complete in total, the installation of the solar panels took fewer than 10 days. “It’s a good idea for a lot of reasons, one of the biggest being that electricity rates are skyrocketing,” Mills said. “This is a really novel way to use available grant funding.” Project funding came from the Washington Climate Commitment Act. Mills said because the CTUIR has “usual and accustomed lands” in Washington, it’s eligible to receive the climate funds, despite the reservation being in Oregon. Power Northwest installed the array. Erik Beeman, a project manager with Power Northwest, said the array features 200 panels weighing about 70 pounds each that can produce up to 590 watts of power per panel, or 118 kilowatts from the whole array, in a day. He said a typical home solar setup produces 6 to 8 kilowatts in a day. “They sized the system, inverters and equipment to be able to accept future solar here, as well,” Beeman said. “That was the idea of the layout and sizing. It makes it quicker and easier to add more solar in the future.” Bruce Zimmerman, tax administrator for the Department of Economic and Community Development, said they chose the food distribution center to power with solar first because it uses so much electricity to keep food cold. In the event the power goes out, there’s a backup generator ready to kick on to keep food frozen or refrigerated. “We felt that to hold those operating costs to a minimum is really important from a utility bill standpoint, so that’s why this was the first project that we designated to put solar in place,” he said. Now that the panels are in, the tribes expects the building’s utilities to be almost entirely offset. In fact, when there is additional electricity produced, it will be donated to Pacific Power to help offset costs for low-income households.
0 Powered by : SKYWORTH PV, a China based solar technology manufacturer, has introduced the SUN Mate balcony photovoltaic system targeting residential users across Europe. The solution is designed for apartment residents and urban households seeking simplified distributed solar installation. The system integrates all-black TOPCon modules, microinverters, and dedicated mounting structures into a unified plug-and-play configuration. It supports flexible installation across various residential layouts without complex construction requirements. SUN Mate offers multiple mounting options, including railing mounting for vertical balcony optimization, sunshade mounting that combines shading and generation and ground mounting suitable for terraces and gardens. The modular architecture enables customized system configuration based on spatial constraints and user requirements. Designed for small-scale distributed generation, the solution focuses on simplified grid connection and high-efficiency power output. SKYWORTH PV will showcase the SUN Mate system alongside other residential solar and storage solutions at Expo Greater Amsterdam from March 10–12, 2026, at Booth A9.
A final decision is due on a proposed £800 million solar farm near the Cotswolds. The 840MW Botley West scheme would cover more than 2,000 acres in Oxfordshire, including land on the Blenheim Palace estate. Planning inspectors have sent their report to Energy Secretary Ed Miliband, but their recommendation has not yet been made public. The Botley West proposal would span approximately 1,000 hectares across three locations: north of Woodstock, west of Kidlington, and west of Botley. The developer, Photo Vault Development Partners (PVDP), has said the project is vital to meet the UK’s climate targets and energy needs, claiming it could power 330,000 homes. Solar panels would remain in place for around 40 years before the land is returned to agricultural use. The scheme has divided opinion. Calum Miller, MP for Bicester and Woodstock, said: “Botley West, one of the largest solar farms ever brought forward in Europe, would have a profound and long-lasting impact on a rural area.” Read more Long-awaited new GP surgery in town on edge of Cotswolds given green light Couple ‘paying the price’ for new home after Cotswolds construction halted David and Victoria Beckham propose more changes at their Cotswolds home
THINK ALUMINIUM THINK AL CIRCLE France-based energy and petroleum firm, TotalEnergies has entered into two long-term Power Purchase Agreements (PPAs) to supply 1 GW of solar power capacity to Google’s data centres in Texas, equivalent to 28 TWh of renewable electricity over a 15-year period. Also Read: UAE-backed Masdar advances solar cooperation with Kyrgyzstan The electricity will come from two TotalEnergies-owned solar projects currently under development in Texas – Wichita (805 MWp) and Mustang Creek (195 MWp). Construction at both sites is scheduled to begin in the second quarter of 2026, according to the company’s statement. The newly signed 1 GW agreements complement an additional 1.2 GW of gross PPAs secured by Clearway, a California-based renewables company that is 50 per cent owned by TotalEnergies. These agreements will support Google’s operations across the ERCOT (Texas), PJM (Northeast), and SPP (Central) electricity markets, strengthening renewable supply across multiple US regions. Marc-Antoine Pignon, Vice President – Renewables, US for TotalEnergies, said: “We are pleased to sign these agreements to supply renewable electricity to Google in Texas, representing the largest renewable PPA volume ever signed by Total Energies in the United States. This highlights TotalEnergies’ strategy to deliver tailored renewable energy solutions that support the decarbonisation goals of digital players, particularly data centers. Through this PPA, TotalEnergies is also addressing the challenges of land availability and power supply for data centers by enabling large-scale colocation opportunities.” Will Conkling, Director of Clean Energy and Power at Google, remarked: “Supporting a strong, stable, affordable grid is a top priority as we expand our infrastructure. Our agreement with TotalEnergies adds necessary new generation to the local system, boosting the amount of affordable and reliable power supply available to serve the entire region.” TotalEnergies currently operates 10 GW of onshore solar, wind, and battery storage capacity in the US, including 5 GW in ERCOT and 400 MW in PJM. Must read: Key industry individuals share their thoughts on the trending topics Responses This website uses cookies We use cookies from our users to operate this website and to improve its usability. You can find details of what cookies are, why we use them and how you can manage them in our Cookies page. Please note that by using this site you are consenting to the use of cookies. Necessary cookies help make a website usable by enabling basic functions like page navigation and access to secure areas of the website. The website cannot function properly without these cookies. Preference cookies enable a website to remember information that changes the way the website behaves or looks, like your preferred language or the region that you are in. Statistic cookies help website owners to understand how visitors interact with websites by collecting and reporting information anonymously. Marketing cookies are used to track visitors across websites. The intention is to display ads that are relevant and engaging for the individual user and thereby more valuable for publishers and third party advertisers. Cookies are small text files that can be used by websites to make a user’s experience more efficient. The law states that we can store cookies on your device if they are strictly necessary for the operation of this site. For all other types of cookies we need your permission. This site uses different types of cookies. Some cookies are placed by third party services that appear on our pages. Your consent applies to the following domains: google.com, youtube.com, doubleclick.net, zopim.com
A historic abbey has been given £200,000 to support an ongoing £5m heritage project to install a ground source heat pump and solar panels. The Delapré Abbey Preservation Trust in Northampton is currently restoring and renovating its 19th Century stables into a multi-purpose space for wellbeing, retail and events. It said that, once completed, it could save about £13,022 a year in energy costs. The cash has come from the South Midlands Growth Hub (SMGH) which said it was to "support projects that strengthened innovation". Ruth Roan, a SMGH manager, said: "Our aim was to support projects that strengthened innovation, supported decarbonisation ambitions and assisted commercial opportunities." She said the project, called "A Stable Future", met that criteria and "will help to secure a financially sustainable future". Richard Clinton, chief executive of the Delapré Abbey Preservation Trust, said it was "a nationally important site that means so much to so many people and this project which will help us to preserve its history and enable us to create a space that nurtures community, wellbeing and sustainability for generations to come. "With this project we're also aiming to demonstrate how low-carbon technologies can be successfully integrated in a complex heritage setting to improve productivity, resilience and long-term sustainability. "The ground source heat pump and solar photovoltaic system we're installing thanks to this funding will help us to realise this ambition." The trust said the green scheme would deliver 32,500 kilowatt hour (kWh) of savings per year and an overall carbon reduction of 8,575 tonnes, that would strengthen its "financial resilience". Work started in the summer of 2025 and should be completed by the summer. Follow Northamptonshire news on BBC Sounds, Facebook, Instagram and X. Gabriel Enyi took months to find the courage to speak to Uloma Igwe, he says. KidsAid is awarded a lottery grant to provide mental health services to schools. Mateo Jeannesson, a Team GB Olympic skier, attended two weeks of trampoline training last summer. The confiscated goods will be destroyed, officers say. The homes are "an unusual solution to a very urgent problem", the council says. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking.
Abundant sunshine. High 58F. Winds WNW at 5 to 10 mph.. Clear to partly cloudy. Low 31F. Winds light and variable. Updated: February 13, 2026 @ 12:54 pm FILE – Mark Munyua, CP solar’s technician, examines solar panels on the roof of a company in Nairobi, Kenya, Sept. 1, 2023. FILE – Solar panels are seen on the roof of a company in Nairobi, Kenya, on Sept. 1, 2023. FILE – Mark Munyua, CP solar’s technician, examines solar panels on the roof of a company in Nairobi, Kenya, Sept. 1, 2023.
FILE – Mark Munyua, CP solar’s technician, examines solar panels on the roof of a company in Nairobi, Kenya, Sept. 1, 2023. FILE – Solar panels are seen on the roof of a company in Nairobi, Kenya, on Sept. 1, 2023. FILE – Mark Munyua, CP solar’s technician, examines solar panels on the roof of a company in Nairobi, Kenya, Sept. 1, 2023. NAIROBI, Kenya (AP) — Africa was the world’s fastest-growing solar market in 2025, defying a global slowdown and reshaping where the momentum in renewable energy is concentrated, according to an industry report released in late last month. The report by the Africa Solar Industry Association says the continent’s solar installed capacity expanded 17% in 2025, boosted by imports of Chinese-made solar panels. Global solar power capacity rose 23% in 2025 to 618 GW, slowing from a 44% increase in 2024. Javascript is required for you to be able to read premium content. Please enable it in your browser settings. Copyright 2026 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed without permission. Sorry, there are no recent results for popular images. Sorry, there are no recent results for popular commented articles. Sign up now to get our FREE breaking news coverage delivered right to your inbox. First Amendment: Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise thereof; or abridging the freedom of speech, or of the press; or the right of the people peaceably to assemble, and to petition the Government for a redress of grievances. Your browser is out of date and potentially vulnerable to security risks. We recommend switching to one of the following browsers:
The public is set to have its say on plans for a controversial new solar park north of the M4 in Wiltshire. Island Green Power has submitted plans to build a 500-megawatt solar farm, known as Lime Down Solar Park, between Malmesbury and the M4. Nearly 5,000 people have written to the Planning Inspectorate about the plans, with the majority objecting to it. The first public hearing into plans will be held in April when a panel of inspectors will listen to arguments for and against the development. A recommendation will then be made to the secretary of state for energy, Ed Milliband, who will make the final decision on the plans, according to the Local Democracy Reporting Service. Lime Down Solar Park would be four miles (6.4km) wide and two miles (3.2km) long, with solar panels that would stand at 14.7ft (4.5m) tall. The scheme has faced huge criticism from locals who have voiced concerns over loss of agricultural land, the scale of industrialisation and flood risk. Other reasons for objection include loss of public rights of way, noise and light pollution, and over construction traffic using narrow rural roads. Wiltshire Council has also objected to the plans, citing "significant unresolved concerns". The Planning Inspectorate will hold a preliminary meeting at Neeld Community & Arts Centre in Chippenham on 21 April. It will be followed by the first open floor hearing, during which interested parties can make oral representations to the inspectors. The first of a number of issue-specific hearings – this one relating to the scope of the project – will be held at the same venue the following day. The Planning Inspectorate has given a provisional date of October 21 for the close of the examination stage. Follow BBC Wiltshire on Facebook, X and Instagram. Send your story ideas to us on email or via WhatsApp on 0800 313 4630. Opponents say the fact nearly 5,000 people have commented should "give serious pause for thought." Womad will return for the first time since 2024 having received a licence to operate. Parents claim staff did not react with urgency when an inflatable house collapsed on young children. Daniel's Well in Malmesbury is to be preserved for generations to come. Wiltshire Council says it has "significant unresolved concerns" about the Lime Down solar farm. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking.
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Almost 6,500 sq ft (600 sq m) of rooftop solar panels have been installed at a council-run garden centre, in an effort to cut energy costs and carbon emissions. The 205,000 sq ft (19,000 sq m) Arium in Whinmoor, Leeds, is believed to be the largest council-run plant nursery in the country, having been operated by Leeds City Council since 1956. Officials estimate the panels will generate 119,294 kWh of clean electricity each year – an average home uses 2,700 kWh annually, according to energy regulator Ofgem. Councillor Mohammed Rafique, said the authority was "not only reducing emissions and energy bills, but also creating healthier, more sustainable public spaces for our communities." Plants grown at The Arium provide flowers and plants for display across Leeds. Rafique, the authority's executive member for climate, energy, environment and green space, said the new initiative was "a major step forward in our mission to become the UK's first net zero city". The project forms part of a wider package of corporate solar schemes supported by Great British Energy and the West Yorkshire Combined Authority (WYCA) Mayoral Renewables Fund. The installation of solar panels at the Arium would be followed by gas boilers being replaced by air source heat pumps, officials said. A council spokesperson said more than 30 sites owned by the authority, and in excess of 30 schools in Leeds, were already "partly powered by solar panels". Listen to highlights from West Yorkshire on BBC Sounds, catch up with the latest episode of Look North. The Reform UK administration at Lincolnshire County Council want to remove the 2050 target. A Sheffield Hallam University study found two million people have attached a love lock to a bridge. How much Yorkshire news can you remember from the last seven days? With people across West Yorkshire complaining about potholes, are they worse this year? One apprentice says he has been given opportunities he never thought would be possible. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking.
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Backup batteries are a crucial add-on for both rooftop and plug-in setups, storing cheap electricity via the sun for later use. EnergySage’s team can navigate the path to a great battery at the best price. The panel-pack combo ensures you have blackout protection while reducing reliance on power generated by coal, oil, and gas, which produce harmful air pollution when burned to make power. In Maine, the plug-in measure includes safety provisions to protect utility line workers and prevent overloads. A unit could help ratepayers whose monthly bills will increase $13-17, according to the Morning Star. 💡Go deep on the latest news and trends shaping the residential solar landscape “It’s about giving a person on a third-floor apartment the same power to lower their electricity bill as a homeowner who has a south-facing roof,” Grohoski said during the hearing. Get TCD’s free newsletters for easy tips to save more, waste less, and make smarter choices — and earn up to $5,000 toward clean upgrades in TCD’s exclusive Rewards Club.
SOLAR: Groundskeepers work to maintain the Mangilao Solar PV Park Tuesday Nov. 11, 2025 in Mangilao. David Castro/The Guam Daily Post
SOLAR: Groundskeepers work to maintain the Mangilao Solar PV Park Tuesday Nov. 11, 2025 in Mangilao. David Castro/The Guam Daily Post The Guam Hybrid Land Use Commission and Guam Land Use Commission have each approved applications by Pacific Energy Corp. relative to the construction and operation of photovoltaic power facilities These facilities are part of the 18.4-megawatt renewable energy project awarded to the Pacific Energy Corp. & Landscape Management Systems Inc. consortium by the Guam Power Authority. This is just one of various awards projects of the power utility’s Phase 4 renewable energy initiative. The Pacific Energy project is split into four areas. The GHLUC and GLUC decided applications for projects in the municipalities of Barrigada and Tamuning-Tumon-Harmon on Thursday. The other sites are in Pulantat and Malojloj. The Barrigada project is adjacent to the former Admiral Nimitz Golf Course and is made up of three lots about 11 acres large in total. The GHLUC approved a conditional use permit Thursday to allow for the construction and operation of the solar power facility. The site in Tamuning-Tumon-Harmon municipality is between the new Ukudu Power Plant and Two Lovers Point. The Guam Land Use Commission approved a zone change from Hotel zoning to M1, or light industrial, zoning to facilitate the construction of the solar power facility. According to documents from the Public Utilities Commission, renewable energy power purchase agreements, or REPAs, for the Barrigada and Tamuning-Tumon-Harmon facilities are estimated to cost GPA about $40.4 million and $40 million, respectively, throughout the 25-year base term of the agreements. REPAs for the Pulantat and Malojojo facilities are estimated to cost GPA about $36.7 million and $40.3 million, respectively, for the base term. These costs total to about $157.4 million for all four facilities over the 25-year base term of the REPAs, or about $6.3 million per year on average. “However, this cost will likely be reduced or neutralized by the reduction of GPA’s existing fuel oil costs caused by using renewable energy from the REPAs at issue here,” a PUC counsel report on the facilities stated.
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While the Redditor may have scored the deal of a lifetime, you can easily find some head-turning deals as well. By taking advantage of EnergySage’s free services, the average person can save up to $10,000 on solar purchases and installations. EnergySage even has a helpful mapping tool that shows the average cost of a home solar panel system by state. It also provides details on solar panel incentives for each state, helping you get the best price for rooftop solar panels and snag all of the discounts available to you. 💡Go deep on the latest news and trends shaping the residential solar landscape Meanwhile, pairing a battery storage system with your solar setup is one of the best ways to protect your home during power outages, save money on energy, and go off-grid for electricity. EnergySage’s free tools can help you learn more about home battery storage options, including competitive installation estimates. Get TCD’s free newsletters for easy tips to save more, waste less, and make smarter choices — and earn up to $5,000 toward clean upgrades in TCD’s exclusive Rewards Club.
Alabama is becoming one of the southern hubs for AI data centers, as our elected leaders seek to be on the cutting edge of future technology while providing economic opportunity that encourages growth and expansion. But to what end? Alabama’s diversity and beauty have long been appreciated and, for the most part, protected from erosion. With the proposed expansion of two new data centers, citizens are engaging, particularly since it will ultimately affect both the landscape and energy prices. In 2024, META (Facebook) received Gov. Kay Ivey’s blessing to open a huge AI data center in Montgomery. Governor Kay Ivey announced today that technology company Meta Platforms plans to open an $800 million data center in Alabama’s capital city that will support 100 operational jobs and build on the company’s previous investment in the state. Meta’s new 715,000-square-foot, AI-optimized data center will be built off Interstate 65 in Montgomery…. It will join the company’s other Alabama data center campus, located in Huntsville and representing an investment commitment of $1.5 billion. As WABE (in partnership with Grist) asserts in this article, META claimed solar energy would power these facilities, helping them remain net zero in terms of emissions. This all sounds well and good, as no one wants to see brownouts, blackouts, or price increases. However, the takeover of arid and productive farmland to accommodate the solar energy required to power these centers has become a presiding problem. “With the rapid advances and uses of AI technology, hyperscale data center projects are popping up all across the Southeast, including here in Alabama,” Alabama Rivers Alliance noted. “These facilities require hundreds of acres of land and buildings to house vast amounts of computers requiring unprecedented amounts of energy and water to operate.” The Alliance further reports that Alabama’s antiquated water infrastructure and laws hinder further expansion and development. This does not appear to concern town councils, let alone the county commissioners who are helping ink these deals. From all appearances, it is full steam ahead, with any consequences addressed later. Alabama Rivers Alliance is working with several Alabama communities to stall or limit the development of these data centers. The other data center in development, “Project Marvel,” was approved for Bessemer in 2025. In January it came under fire because developers initially presented one plan, but newer plans show a takeover of more infrastructure, which in turn will require more energy expansion. Despite the progress made, there is a major setback facing the Project Marvel data center in Alabama…. The data center has been met with fierce opposition since it was introduced and locals are against the project. Public Facebook group ‘Bessemer Data Center-We say NO’ has been vocal about its opposition to the proposed project. The legitimate concerns expressed are the same of other U.S. states who are opposed to their data centers, including “potential environmental impact and the possibility of rising utility bills such as electricity. … Some highlight the data center boom pushes into communities that have little to no insight with such projects.” These tensions are legitimate and cannot be ignored. When similar centers were built in Georgia, officials promised that solar would be their only source of power. Yet new reports show that Georgia is pulling from other forms of energy to support the exorbitant amount of power these data centers use. Citizens wonder what will happen to their communities, let alone their resources, once these data centers are established. It may already be too late. Stockton is the latest community questioning solar expansion in support of these centers, 1819 News reports. Nashville-based Silicon Ranch is attempting to develop a 2,000-acre solar farm on a private, 4,500-acre property near the Tensaw River Delta wetlands. The project would support a Meta data center near Montgomery. The project has raised concerns about environmental impact and a lack of communication from officials. The Alabama Public Service Commission (PSC) approved the project on Dec. 2, 2025, but residents said they didn’t learn of it until last week. ‘Here we are, a massive grassroots civilian group who is quickly organizing and putting their heads and resources and connections together to find a way to stop this unwanted project that giant corporations and some sneaky politicians cooked up,’ said organizer Meagan Fowler. Politicians and local officials see dollar signs and more “modern” citizens who will support their candidacies and campaigns. Residents, however, see the destruction of both their communities and the beauty of the environment and the wildlife it draws and supports. Those who have traveled to the Midwest or West have seen the wind turbine farms peppering the landscape. These “Skynet”-like structures are not only a blight to the skyline, but they kill birds and affect the ecosystem of the land. Solar panels present similar issues, not only destroying once-rich soil, but affecting wildlife and the surrounding areas. In September, I wrote about the issues with a small solar panel farm in Muscle Shoals, which has resulted in the flooding of farmland and residential homes. Despite that, and a lawsuit lodged against the city over the flooding, town officials plowed ahead with the solar farm expansion, which includes neighboring Sheffield. The Shoals area is fast becoming a Huntsville suburb, thanks to the two AI data centers now housed in the Rocket City. City leaders claim that the production will mitigate the increased demand from Tennessee Valley Authority as the region expands. Many of the Shoals residents are not convinced, but unlike Stockton, citizen engagement is sorely lacking. Yet the communities south of the Shoals are in the thick of this battle between progress and preservation, so we will be watching it closely. Jennifer Oliver O'Connell, As the Girl Turns, is an investigative journalist, author, opinion analyst, and contributor to 1819 News, Redstate, and other publications. Jennifer writes on Politics and Pop Culture, with occasional detours into Reinvention, Yoga, and Food. You can read more about Jennifer's world at her As the Girl Turns website. You can also follow her on Facebook, Twitter, and Telegram. The views and opinions expressed here are those of the author and do not necessarily reflect the policy or position of 1819 News. To comment, please send an email with your name and contact information to [email protected]. Don’t miss out! Subscribe to our newsletter and get our top stories every weekday morning.
• Mitsubishi can help you find efficient heating and cooling solutions for your home and connect you with trusted installers • Not ready to spend up front? Palmetto‘s $0 down HVAC leasing program can lower your energy costs by up to 50% • TCD’s HVAC Explorer makes it easy to access exclusive offers from preferred partners Palmetto offers HVAC leases starting at $99 per month and including 12 years of free maintenance. When you pair an updated HVAC system and other efficient appliances with solar panels, your utility costs drop even lower. TCD’s Solar Explorer can help you find a solar system that works with your budget. Another resource is the free Palmetto Home app, which connects you to up to $5,000 in rewards that you can spend on home efficiency upgrades. “So, are heat pumps worth it in Illinois? The short answer is yes, at least for many homeowners,” another HVAC company, Trust Heat Cool, wrote. “Today, modern models are capable of meeting the state’s climate needs while paying huge dividends in energy savings and environmental progress.” Thom Dunn of The New York Times wrote: “Electric heat pumps can also help to reduce carbon emissions in every U.S. state by up to 93%, while still providing two to five times more heating energy than the energy you put into it, on average. As a result, a heat pump is an environmentally friendly HVAC system that will also save you money.” Get TCD’s free newsletters for easy tips to save more, waste less, and make smarter choices — and earn up to $5,000 toward clean upgrades in TCD’s exclusive Rewards Club.
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If you want to drive utility costs even lower, you can pair your solar panels with electric appliances. For instance, a high-efficiency HVAC unit can offer drastic savings on heating and cooling. Mitsubishi can help you find the right one for your home and budget, and you can earn up to $5,000 for home upgrades by completing simple challenges through the free Palmetto Home app. Get TCD’s free newsletters for easy tips to save more, waste less, and make smarter choices — and earn up to $5,000 toward clean upgrades in TCD’s exclusive Rewards Club.
News This is a news story based on facts, either observed and verified firsthand by the reporter, or reported and verified from knowledgeable sources. Share Monitor Local is a reporting initiative to meet the local information needs of people in towns across western and downeast Maine with public service reporting on town council meetings, school budget debates, zoning conversations, tax deliberations and more. It functions alongside The Maine Monitor’s investigative and in-depth reporting to serve the people of Maine. PARIS — The Select Board voted Wednesday night to approve a proposal from a North Carolina‑based solar company to build a solar farm on the capped AC Lawrence Sludge Landfill off Kilgore Road. Richard Jordan, senior project developer at Paddle Energy, a North Carolina‑based company with offices in Bangor and Yarmouth, and Lauren Leclerc, an environmental consultant with Flycatcher LLC, told the board the project, called Baxter, would produce about a megawatt of solar energy. “We are proposing to use the existing landfill in its entirety,” Jordan told the board. Jordan said an existing gravel access road maintained by the town would provide access to the solar site, though Paddle Energy would build a small extension to it. According to the application, the site would connect directly to Central Maine Power Co. lines northwest of Kilgore Road. The landfill was used by AC Lawrence Leather Co., a now‑defunct business based in Peabody, Massachusetts, as a sludge disposal site until at least 1980. According to a U.S. Environmental Protection Agency profile on similar sludge lagoons on Oxford Street, sludge was removed from the lagoons and placed in nearby landfills, “which was later capped under state regulations and the site backfilled with clean fill.” Paris Code Enforcement Officer Chris Summers said he has visited the landfill twice in the past four years to evaluate the condition of the cap. “When you have a landfill, the DEP often looks for spots where the ground has collapsed because either water is washing down through, and there was nothing,” Summers said. “The site looks pretty good. Everything that I’ve seen says, yeah, this is holding up really well.” Jordan said the panels will be placed on ballasts, which will be filled with concrete or rock. “They can be placed directly on the landfill without impacting the surface,” he said. Chief Mark Blaquiere of the Paris Fire Department said another Paddle Energy–owned solar site at a former landfill caught fire in the past few months. Jordan said the new location would have a Knox Box — a small safe containing keys so firefighters can access the site during an emergency. “We will coordinate for training of the Fire Department staff as well,” Jordan said. Summers noted that solar farms are a good use for landfills, which can be difficult to develop for other purposes. “There’s not a lot you can do with the landfill without limiting availability for anything else,” he said. “A lot of people honestly aren’t interested in the landfill because you don’t know what is in there.” The Select Board is scheduled to walk the property Wednesday, Feb. 18, and a public hearing on the project is set for Feb. 25. The Maine Monitor is committed to deeply researched, nonpartisan reporting that informs Mainers about issues of public interest, holds institutions accountable, and profiles solutions. Our reporting takes time, but we believe it is worth doing because it is critical to a functioning society and democracy. The Maine Monitor is a nonprofit newsroom that relies on the contributions of our community to sustain our in-depth, independent, free to read journalism. If you value this type of reporting, please consider donating and becoming a part of the community that makes this reporting possible. Report an error | Contact the newsroom | Republish our stories Jon Bolduc is an educator, writer and journalist who currently resides in Lewiston and works in the Oxford Hills as a middle school journalism teacher. He graduated from the University of King’s College with a bachelor’s degree in journalism in 2015 and previously worked as a staff reporter at the Sun Journal and Advertiser Democrat from 2018 to 2020. He loves coffee, cats, the outdoors, and teaching young journalists. Contact Jon via email: gro.r1771066424otino1771066424menia1771066424meht@1771066424noj1771066424 Don’t Miss These Stories P.O. Box 284 Hallowell, ME 04347
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Published 10:00 pm Thursday, February 12, 2026 By BERIT THORSON | East Oregonian MISSION — When the sun beats down on Coyote Business Park during an Eastern Oregon summer, from now on, some of that sunlight will be transformed into electricity. The Department of Economic and Community Development with the Confederated Tribes of the Umatilla Indian Reservation finished a solar array project at the center for the Food Distribution Program on Indian Reservations. The project is part of tribal efforts to increase renewable energy use. Patrick Mills, a certified energy manager and project management professional with the department, said while the array took about nine months to complete in total, the installation of the solar panels took fewer than 10 days. “It’s a good idea for a lot of reasons, one of the biggest being that electricity rates are skyrocketing,” Mills said. “This is a really novel way to use available grant funding.” Project funding came from the Washington Climate Commitment Act. Mills said because the CTUIR has “usual and accustomed lands” in Washington, it’s eligible to receive the climate funds, despite the reservation being in Oregon. Power Northwest installed the array. Erik Beeman, a project manager with Power Northwest, said the array features 200 panels weighing about 70 pounds each that can produce up to 590 watts of power per panel, or 118 kilowatts from the whole array, in a day. He said a typical home solar setup produces 6 to 8 kilowatts in a day. “They sized the system, inverters and equipment to be able to accept future solar here, as well,” Beeman said. “That was the idea of the layout and sizing. It makes it quicker and easier to add more solar in the future.” Bruce Zimmerman, tax administrator for the Department of Economic and Community Development, said they chose the food distribution center to power with solar first because it uses so much electricity to keep food cold. In the event the power goes out, there’s a backup generator ready to kick on to keep food frozen or refrigerated. “We felt that to hold those operating costs to a minimum is really important from a utility bill standpoint, so that’s why this was the first project that we designated to put solar in place,” he said. Now that the panels are in, the tribes expects the building’s utilities to be almost entirely offset. In fact, when there is additional electricity produced, it will be donated to Pacific Power to help offset costs for low-income households.
From our collaborating partner “Living on Earth,” public radio’s environmental news magazine, aninterview by host Steve Curwood with journalist Isabel Hilton. As the United States fully withdrew from the United Nations climate negotiations in the fall of 2025, China stepped forward with an absolute emissions-reduction target of at least 7 percent by 2035. While the U.S. is the world’s largest historic emitter of greenhouse gases, China is the largest present-day emitter. With the U.S. now gone from the negotiating table, China is effectively in charge of the terms of international climate agreements. And since energy drives so much of modern commerce, China is already seizing the moment to develop its economy by supplying the world with the clean technologies of the future, as the U.S. lags behind. Analysis by Carbon Brief shows that in 2025 solar power, electric vehicles and other clean-energy technologies powered more than a third of China’s gross domestic product growth at the same time the U.S. economy had lower growth and higher inflation. Isabel Hilton is a former BBC journalist and founder of Dialogue Earth, which started as China Dialogue. This interview has been edited for length and clarity. We deliver climate news to your inbox like nobody else. Every day or once a week, our original stories and digest of the web’s top headlines deliver the full story, for free. Our #1 newsletter delivers the week’s climate and energy news – our original stories and top headlines from around the web. Dan Gearino’s habit-forming weekly take on how to understand the energy transformation reshaping our world. A once-a-week digest of the most pressing climate-related news, written by Kiley Price and released every Tuesday. Don’t miss a beat. Get a daily email of our original, groundbreaking stories written by our national network of award-winning reporters. Go behind the scenes with executive editor Vernon Loeb and ICN reporters as they discuss one of the week’s top stories. A digest of stories on the inequalities that worsen the impacts of climate change on vulnerable communities. STEVE CURWOOD: What is China’s political and economic position in the world today, given the U.S. has abandoned the international negotiations and declared an end of federal support for climate mitigation and adaptation? ISABEL HILTON: China’s position in terms of climate negotiations is stronger than it ever was. China remains a very big emitter, but it’s also the world’s second-largest economy. It’s the largest trading partner of dozens of countries around the world, and it’s now the biggest supplier of low-carbon goods and everything you need for the energy transition. It has a virtual monopoly position on a lot of those technologies, and it is the biggest installer of clean energy by far in the world. Last year it installed half of all the clean energy installations globally. So it’s a real leader, and it remains committed to climate negotiations. There’s no climate-denial problem in China. There is an issue around responsibility, how fast China is going to move, when it’s going to peak and how fast it will draw down its own emissions. But in terms of the process, it’s a very big and central player these days. CURWOOD: How successful is China now in making its transition to renewables in its economy? I understand they’re still building coal plants there. HILTON: They are building coal plants, and there are a number of reasons for that. As we came out of the pandemic, there were two successive years in which, for different reasons, there were widespread power cuts in China, just as they were trying to get the economy off the ground. If you’re a provincial governor in China, you have targets to meet, you have an economy to grow and losing power is not helpful, so you essentially want to have your form of energy security. So while we’ve seen a huge growth in electrification in China and a surge in electricity demand which has substantially been met by renewables, we still have the anxiety of what happens if there is a drought and there’s no more hydropower? What happens if, for whatever reason, we lose supplies of gas and oil? The thing that China has in super abundance is coal. They are very efficient plants, and they are now saying that they’re using them largely for the capacity market, which is slightly unconvincing, but that means that they’re not going to have the same old system where they’re committed to buy X amount of energy per year from the coal plants, which meant that they got priority access to the grid. What they’re now saying about coal plants is that they will prioritize renewable energy, and they’ll use the coal plants for backup when they need them. So that’s the story. I’m not entirely convinced, but that’s the excuse, if you like. The other important thing is that the coal industry is very big in China, so you have a couple of provinces in the north that are almost entirely dependent on coal for their economies. It’s quite hard to shut down vested interests that are quite that big. So it will go slowly. Building new coal is not helpful, but we have to recognize that China has politics too. CURWOOD: China is a leader now in renewable energy. How did it get there? To what extent did horrible air stimulate that move? HILTON: Bad air was certainly around when the decision was made. In the first decade of this century, you had China with an economic model that was beginning to fail. It was the catch up, very rapid growth, “let’s go for GDP growth at all costs,” and that works for a while. You’re making a lot of cheap, low-added-value goods. But after a while that runs out of steam. You’ve used up all your first advantages, and you have to get more efficient. You have to move up the technology chain if you’re going to go on growing. Otherwise, you get stuck in the middle-income trap. So China was looking at this, thinking, what are the technologies of the future? At a time when there was also terrible air. Pollution was a thing. People were very, very unhappy; there were big demonstrations. But also the Chinese realized that climate change was real and that China was going to be impacted heavily by it. But also, if the world was going to make a transition to clean economies, it was going to need technologies, and China decided to combine industrial ambition, economic ambition and scientific realism and invest enormously in every aspect of every technology that was going to be required for renewables. So that was wind energy, solar, carbon capture, nuclear power. There’s pretty much everything that you can think of that the 21st century is going to need. And China decided that it was going to be the world’s dominant purveyor of those goods and technologies, and it bet the economy on those with great success. CURWOOD: Historically, the West got very rich with fossil fuels. The economy really built up with the fossil fuel economy. Given that history and China’s advance in the area of renewable energy, what does this put the United States and China vis a vis each other when it comes to economic growth and competition? To what extent is China in a position to eat America’s lunch now for further development? HILTON: That’s certainly what it looks like because the United States has big incumbent industries, has a lot of relatively cheap fossil fuel, and industries want to defend their interests. The U.S. has been a very stop-start player in climate right from the beginning. The current administration is probably certainly the worst, but right from the start, the United States has been, you know, not entirely a helpful player. It had its good moments, and it had its not-so-good moments, like signing up for Kyoto, then not ratifying it and so on. It is unfortunate for the world that the United States is such a big emitter. “China decided to combine industrial ambition, economic ambition and scientific realism and invest enormously in every aspect of every technology that was going to be required for renewables.” It’s unfortunate for the United States that it’s turning its back on the future. If you look at all the technologies that China now dominates—because it’s a very efficient manufacturer and has secured its supply chains—it has managed to lower the cost of those technologies. So now it’s actually cheaper to generate renewable electricity than it is to generate any kind of power with fossil fuels, and all the technologies that ride on the electric economy, and that includes electric vehicles. It includes all forms of transport. There will at some point be an electric plane. All of these things and all the associated technologies, like amazing battery technologies, are now dominated by China. Europe and the United States are not short of innovation, but China has scale. It has an enormous domestic market and it has a planning system which committed the entire economy to go in that direction. The fact is that it’s very hard now to compete with China, and if the United States draws back from all this sector, it’s going to be very, very hard to catch up, in my view. CURWOOD: One of the ways China has been asserting itself as the dominant force in the clean energy future is by forging trade partnerships with other nations, including Canada, which recently cut tariffs on Chinese EVs from 100 percent to just 6 percent. What does that deal mean both in terms of geopolitics and economics? HILTON: It’s obviously distressing for the Canadian motor industry, and it raises another set of concerns. If you look at the politics of energy these days, we used to talk about energy security in terms of a reliable supply at a reasonable price. So you secured your oil wherever you secured it, and when the prices went up, your economy suffered. Now, energy security in a renewable age is a given. Supply is a given because you install your wind or your solar energy, and you have storage. There is no problem of supply, but there is a problem that all these technologies remain connected. Particularly electric vehicles remain connected to their manufacturer; they’re intelligent machines. The difficulty with that is that it opens up a whole other set of security concerns when the origins of those technologies are not in a country that you can reliably assume is a friendly country, and that is the case with China. We all have relations with China, but it is in some ways potentially an antagonistic power. In Europe we are deeply concerned about the China-Russia relationship because of Russia’s invasion of Ukraine. So there are all sorts of questions about security, about energy security, which are to do with critical national infrastructure and access to the grid and the collection of data and the capacity to hit a kill switch, which are embodied in things like electric vehicles. There is a whole parallel conversation going on about how you secure your systems with those technologies, or can you? CURWOOD: What you’re alluding to is the prospect that, say, a Chinese electric vehicle might have a circuit in it that could be activated that shuts it down. And if you had a whole bunch of those vehicles, they might just be stuck [on] the side of the road because somebody doesn’t like it, sort of the way that the satellite telephony stuff that Elon Musk has, it’s been used in Ukraine, can also be shut down at a moment’s notice, that Starlink can just go away. Am I talking about the right set of concerns here? HILTON: You are. There’s very likely to be a kill switch because the manufacturer needs to upgrade the technology. This connection has to be maintained. You get software updates over the air, you get firmware updates over the air, so that’s kind of a given. There is also the possibility that a car could be hacked and accessed and made to commit an act of urban terrorism. A driverless car could be used as a terrorist vehicle. There are all sorts of security concerns about these technologies, and they are now being mapped on to industrial concerns. If you look at how the Chinese treat, for example, a Tesla in China, all the data has to stay in China, and there are places you’re not allowed to drive that car. You’re not allowed to drive it near a military base. You’re not allowed to drive it around town if a leader like Xi Jinping is visiting. So the Chinese are very well aware that there are security issues around electric vehicles. Our nonprofit newsroom provides award-winning climate coverage free of charge and advertising. We rely on donations from readers like you to keep going. Please donate now to support our work. CURWOOD: How important are certain minerals that the Chinese seem to have a pretty good hold of when it comes to the renewable energy business? To what extent are U.S. tech industries, as well as other industries, dependent on China for this material? HILTON: The West abandoned the processing of critical minerals to China, because it’s a very dirty process. China has been de-risking its relations with the rest of the world since 2015, and part of the strategy of de-risking was to secure supply chains. So it made a concerted effort to source critical minerals all over the world, particularly in Congo or in Chile, in the lithium triangle in Latin America. But it’s not just the mining, it is particularly the processing that China virtually monopolizes, and that is going to take some time for Europe or the United States to substitute, because we have left the technology to the Chinese. They’ve got very good at it. And we would be starting from scratch. Other countries would be starting from scratch. So although sourcing of these minerals is not a major problem, rendering them useful is and they are absolutely essential. They’re essential to batteries. They are essential to the defense industry. Even if the United States is turning its back on renewables, at least at the official level, it has a defense industry. Everything you drive, everything you fly, uses these critical minerals. So it’s a very, very big and potentially powerful monopoly that China has at the moment. CURWOOD: Those minerals were also used in the iPhones or the smartphones, right? HILTON: They’re in everything. Your house is full of them. Your pocket has quite a few. They are, in a way, as essential to the contemporary economy, to the digital world, as oil or coal was to the old industrial world. CURWOOD: To what extent is progress on the climate a political, ideological warfare matter between the U.S. and China? HILTON: It’s very hard to understand why the U.S. administration has taken the turn that it did. It’s quite clear that the Chinese decided to build their capacity in renewable technologies, and they did it with great success. The benefits to the world are that they have lowered the price of all these technologies to the point that the price barrier has virtually gone and that means that countries that have yet to build their energy systems don’t have to go through the high-emitting fossil fuel stage. They can go straight to renewables. Now, if you’re in the oil business, that’s a threat. If you’re in the coal business, that’s a threat. I don’t think that political pressure from the United States to keep the oil and the coal business going is going to be very successful, because in the end, business is business, and the administration’s efforts to stimulate the domestic coal business in the U.S. didn’t work first time around, and I very much doubt they’ll work this time around, because those days are kind of over. In geopolitical and ideological terms, it’s greatly to China’s benefit to be seen as a responsible climate player. Twenty years ago, China was the bad boy of climate, because it was a very high emitter. It is still a very high emitter. It still needs to get its emissions down, but reputationally, it’s not nearly as bad as it was 20 years ago. Reputationally, it has quite a few cards to play, including its phenomenal installation of renewable energy at home and its supply of cheap and reliable technologies abroad. Perhaps you noticed: This story, like all the news we publish, is free to read. That’s because Inside Climate News is a 501c3 nonprofit organization. We do not charge a subscription fee, lock our news behind a paywall, or clutter our website with ads. We make our news on climate and the environment freely available to you and anyone who wants it. That’s not all. We also share our news for free with scores of other media organizations around the country. Many of them can’t afford to do environmental journalism of their own. We’ve built bureaus from coast to coast to report local stories, collaborate with local newsrooms and co-publish articles so that this vital work is shared as widely as possible. Two of us launched ICN in 2007. Six years later we earned a Pulitzer Prize for National Reporting, and now we run the oldest and largest dedicated climate newsroom in the nation. We tell the story in all its complexity. We hold polluters accountable. We expose environmental injustice. We debunk misinformation. We scrutinize solutions and inspire action. Donations from readers like you fund every aspect of what we do. If you don’t already, will you support our ongoing work, our reporting on the biggest crisis facing our planet, and help us reach even more readers in more places? Please take a moment to make a tax-deductible donation. Every one of them makes a difference. Thank you, David Sassoon Founder and Publisher Vernon Loeb Executive Editor We deliver climate news to your inbox like nobody else. Every day or once a week, our original stories and digest of the web’s top headlines deliver the full story, for free. Tailpipe standards meant to hasten adoption of electric vehicles were slashed alongside the scientific basis for regulating greenhouse gas emissions. That will come at a cost. By Marianne Lavelle, Dan Gearino ICN provides award-winning climate coverage free of charge and advertising. We rely on donations from readers like you to keep going.
Jaipur-based Solar Plug Consultants has secured a detailed engineering mandate for a 248 MWp DC/200 MW AC solar project with 50 MWh BESS, strengthening its footprint in the utility-scale renewable segment. February 14, 2026. By Mrinmoy Dey Can LNG & EVs Successfully Coexist in India’s Freight Ecosystem? Tells UGEL's MD Maqsood Shaikh Smart Meters Central to RE Integration & Demand-Side Management in India, Says Jitendra Agarwal Sungrow is Targeting 60 GW+ Milestone in India: Harendra Tomar Sean Matthew Explains How Surface Engineering is Becoming Core Strategy for Indian Refineries India’s EV Battery Demand is Shifting from Cost to Value: Pratik Kamdar, Neuron Energy
0 Powered by : The U.S. International Trade Commission (USITC) has received two Section 337 complaints from Trina Solar relating to alleged TOPCon patent infringement. The proceedings target Runergy, Adani, and CSI Solar and are based on claims drawn from US Patent Nos. 9,722,104 and 10,230,009. The investigations were formally launched in November and December 2024 and were consolidated on January 21, 2025. In later proceedings, the patent claims were revised, Mundra Solar PV Ltd. was added as a respondent, and the final target date was extended to August 18, 2026. An administrative ruling granting termination was issued under Order No. 40 on January 15, 2026, followed by the Commission’s decision not to review the termination order, on February 10, 2026.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 16, Article number: 3438 (2026) Cite this article 1053 Accesses Metrics details Machine-learning techniques are widely used across many disciplines, including electricity generation forecasting. In this study, the Support Vector Machine (SVM) based models, one of the machine learning techniques, were developed for daily PV power forecasting. To improve model performance, models were tuned with four metaheuristic optimizers, including the Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Daily PV power and temperature data from 2020 to 2023 were obtained for the Stavanger, Oslo, and Kristiansand regions which located in southern Norway. One of the innovative aspects of this study is the investigation of the performance of SVM (Support Vector Machine) combined with various optimization methods across four alternative input configurations. To examine the different feature combinations, four different input configurations were created through the Minimum-Redundancy Maximum-Relevancy (MRMR) method. The analysis results obtained with SVM were further enhanced using all optimization techniques. Among those, the SVM-PSO-M04 (r = 0.7707, NSE = 0.5748, KGE = 0.7092, PI = 0.2964 and RMSE = 0.6513) method produced the most effective results (improving the correlation coefficient (r) to 0.7707 (approximately a 19% increase over the untuned SVM)) among the tested hybrid configurations obtained in our experiments. Moreover, coupling temperature data alongside PV power as model input also tends to improve forecasting skill. Results of this study provide a case-study benchmark for researchers, institutions, and other stakeholders engaged in renewable energy planning and management in high-latitude regions. Solar energy has emerged as a critical frontier of global decarbonisation strategies. Yet, under climate change, the power capacity of these systems remains fundamentally linked to meteorological conditions that are themselves undergoing a transformation1,2,3,4,5. Long term climate model ensembles consistently project regional scale shifts in both surface solar radiation and near-surface air temperature which jointly determine photovoltaic (PV) efficiency6,7,8,9,10. While solar energy is subject to geographic and temporal variations4,6, the relation between rising temperatures and solar energy production introduces a more complex technical challenge. The linear degradation of photovoltaic conversion efficiency as cell temperature rises is a fundamental issue particularly in urban or low-latitude regions which consequently can offset the benefits from increased insolation even with minor warming conditions9,11,12. A case study in Istanbul exemplifies this negative synergy. The study demonstrated that according to RegCM based projections through 2050, a small decrease in incoming radiation levels may pose a direct threat to future PV yields when coupled with simultaneous multi-degree warming13. For ensuring modern energy grids remain resilient in the face of a rapidly shifting climate accurate solar power forecasting is a fundamental necessity. Future climate conditions, intensified by climate change, are expected to increase the complexity of PV system from initial design through to operation, and maintenance. In Europe and parts of East Asia as the reduction in cloud cover is expected to offsets thermal performance losses, photovoltaic yields are expected to experience modest rise, even under aggressive warming scenarios4,5,7,9,10. Conversely regions like West Africa, North Africa, Australasia and parts of Central Asia are likely to face declines linked to solar dimming and rising cell temperatures, although most studies still find decreases remain within a 6% margin of current baselines4,6,8,10,12. While global case for solar remains as a no regrets investment, site specific planning must integrate a consistent climate signal into long-term energy planning to ensure sustained accuracy when projections are considered1,2,3,5,7. Predictive analytics have become from a support tool to a fundamental need to maintain grid stability and manage net loads effectively as solar integration grows14,15. Beyond immediate operations, solar forecasting plays an important role in the economic profile of projects by optimizing battery storage and reducing wasted energy through curtailments16. It is suggested that integrating solar predictions with storage systems not only lowers operational expenses but also decreases overall grid dependency17. While various modeling techniques, apart from traditional regression, such as advanced deep learning, offer reliable short- and long-term outputs18, recent evidence suggests that the latter has demonstrated superior accuracy for irradiance forecasting19.Considering future climate where solar volatility is expected to intensify, the ability to quantify prediction uncertainty will be beneficial for microgrid resilience and the sophisticated management of modern power infrastructures20. Solar power prediction provides consistent grid operation, storage optimization, economic allocation, and integration of renewable sources. Furthermore, it helps in reducing the impacts of climate-induced variability. Ultimately, these forecasting tools serve as the backbone for consistent, climate-resilient power systems. Recent research on solar-irradiance forecasting spans a wide spectrum of time-horizons and data sources, progressing from purely statistical baselines to highly integrated, image-enhanced deep-learning pipelines. Early work demonstrated the value of classic statistical approaches: Paulescu and Paulescu21 showed that an empirical two-state clear-sky model outperformed random-walk, moving-average and Autoregressive integrated moving average (ARIMA) baselines for four-samples-per-minute data from Timișoara, Romania, while Zambrano and Giraldo22 built multidimensional transfer models that dispense with costly on-site training measurements. Parallel semi-empirical efforts for hourly horizons combined extraterrestrial irradiance and clearness-index signals to surpass the Angström-Prescott formula at several Turkish sites23. Surveys and reviews have mapped the methodological landscape, covering statistical, cloud-image and NWP (Numerical Weather Prediction), routes24 and benchmarking time-series, image and hybrid families25. Machine-learning studies have gradually pushed forecast granularity to the minute scale. Image-only pipelines link real-time sky-camera RGB profiles to 1–10 min global horizontal irradiance with competitive the mean absolute percentage error (MAPE) and the root mean square error (RMSE) scores26; all-sky imagers coupled with simultaneous irradiance readings achieved ramp-event detection indices of 43–62% at a Uruguayan test bed27. Satellite/NWP coupling also remains powerful at the 0–3 h range, trimming persistence errors by ≈ 10 W m⁻² across the U.S. SURFRAD network28. Where on-site imagery is unavailable, short-term physics-free predictors such as Artificial Neural Network (ANN)-SFP29 and daily-scale SVM/ANN/k-NN ensembles30 still yield R² values up to 0.94. Deep learning has become the dominant trend for sub-hourly horizons. For example, CNN–LSTM hybrids (convolutional neural networks combined with long short-term memory networks) that fuse wavelet-packet-decomposed sequences with ground imagery have been shown to lower RMSE compared to back-propagation neural networks (BPNN), Support Vector Regression (SVR), and standalone LSTM models on three U.S. stations31, while multi-modal deep clustering aligns cloud-camera frames with the Numerical Weather Prediction (NWP) fields, reaching a 29.4 W m⁻² day-ahead RMSE in California32. Recurrent architectures remain strong: Deep Recurrent Neural Networks (DRNN) surpassed SVR and feed-forward networks in Canada33; LSTM networks delivered R² > 0.9 under complicated weather in Atlanta and Hawaii34; and bidirectional/attention LSTMs benefited from multi-site NASA POWER inputs across India35. Cutting-edge transformer variants now integrate variational-mode-decomposed components, eclipsing Convolutional Neural Network (CNN)-LSTM and vanilla transformer baselines over a 2015–2019 EMAP data set36. Comprehensive Indian reviews confirm that such CNN, LSTM and CNN-LSTM hybrids can lift accuracy by up to 37%37. Collectively these studies highlight three converging insights for solar-radiation forecasting. First, hybridization, whether statistical-empirical, image-plus-NWP, or signal-decomposition-plus-deep-network, consistently boosts performance across climates. Second, model choice and horizon must reflect data availability: transfer learning and clear-sky filters remain valuable when imagery is scarce, whereas minute-ahead dispatch benefits most from sky cameras and CNN-LSTM fusion. Third, the field is shifting toward interpretable, multi-modal deep architectures that can generalize without local retraining, a direction reinforced by the superior accuracy of multi-modal deep clustering (MMDC)32 and Multiple Image Convolutional Long Short-Term Memory Fusion Network (MICNN-L)25. Remaining gaps include systematic cross-climate validation beyond the U.S., Europe and India, and unified uncertainty quantification to complement RMSE-based metrics. In addition to new DL techniques and ML methods, nature-inspired metaheuristics have also emerged as powerful tools for solving complex nonlinear, multimodal optimization problems, particularly in the domains of machine learning model tuning, feature selection, and system design. Among these, the Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA) are widely recognized for their balance of exploration and exploitation strategies. Those optimization algorithms are used both as a single optimization technique or a combination of various alternatives. ABC algorithm, inspired by the foraging behavior of honeybees, has gained attention due to its simplicity and strong global search capabilities. As a population intelligent algorithm, ABC applied in many studies. Oruc et al.38, Zhang et al.39, Gujarathi et al.40 incorporated ABC directly employed with a ML method or hybridized with another optimization algorithm in studies ranging drought forecast to engine optimization and handling with high dimensional datasets. GWO mimics the social leadership and cooperative hunting strategies of grey wolves. It is valued for its algorithmic simplicity and convergence stability. GWO has been successfully applied in time series forecasting and parameter tuning tasks and demonstrated its effectiveness in optimizing41,42,43. GA is a classical evolutionary algorithm based on principles of natural selection, crossover, and mutation44. It has broad application across optimization tasks and is often used as a baseline for performance comparisons with newer methods. Genetic Algorithms remain to be used in hybrid models to boost convergence rates across problem spaces containing either discrete or continuous parameters in diverse application44,45,46,47. The Whale Optimization Algorithm (WOA) inspired from feeding strategy of whale population48. WOA gained recognition for its simplicity and ability to balance intensification and diversification. Tang et al.49 introduced a combined WOA-ABC algorithm to overcome local optima problem and improve solution accuracy across theoretical and practical engineering problems. Although less frequently cited in academic databases, the Coyote Optimization Algorithm (COA) or COATI represents another nature-inspired method that modeled on coyote pack dynamics and social learning50. Despite showing potential across broad domains ranging from energy planning to photovoltaic parameter extraction51,52, additional comparative studies are needed to validate its performance relative to better-established algorithms like ABC, GWO, and WOA. Similar to other algorithms, since its initial development, COA has evolved into several improved forms, including Chaotic COA (CCOA)53 and Multiobjective COA (MOCOA)54 which have been implemented in a broad range of problem domains. As Norway targets toward broadening its energy mix and reduce its dependence on fossil fuels sources, solar power becomes the increasing component of the energy strategy. Norway now recognizes the importance of solar energy as an important complementary source of renewable electricity generation while it has been historically known for its hydropower capabilities55,56,57. The possibility of using solar energy across Norway’s diverse landscapes, such as urban areas, agricultural regions, and industrial sites, has become much more realistic due to the significant drop in solar PV system prices and progress in solar technology in recent years56,58. As solar energy will not be sufficient to supply Norway especially during the winter months, hybrid systems offer a promising solution for integrating solar energy into Norway’s energy landscape to meet the demands for renewable energy59. While Norway is not typically known for its solar energy production, it possesses a number of hidden advantages for PV performance. The favorable effect of low ambient temperatures on solar panel efficiency counterbalance lower irradiance levels by reducing heat-related efficiency losses since PV systems, especially those based on crystalline silicon, are thermally sensitive—their efficiency decreases by ~ 0.4–0.5% per °C increase in module temperature59,60,61,62,63. Moreover, new applications like icephobic nanocoatings64, considering climate variations and orientations affect65 increasing performance of the systems. Rees et al.,66 also built a simple, transparent workflow that turns freely available high-resolution LiDAR into city-wide rooftop-solar potential estimations and concluded that Rooftop solar PV in Tromsø could realistically supply ≈ 20–30% of the city’s annual electricity demand, about 200 GWh yr⁻¹, with residential roofs alone contributing roughly 40% of that total. In addition, new technologies, including tilting systems, bifacial panels, and heat recovery-integrated PV (PVT), present opportunities to improve year-round utilization67,68,69,70. Given the evolving scientific landscape and Norway’s focus on solar energy, there is a need to integrate advanced solar power forecasting methodologies into regional energy planning and grid management. International studies have exhibited the superiority of hybrid deep learning models, especially those combining sky imagery, NWP outputs, and statistical decomposition. Though, local adoption in high-latitude contexts like Norway remains limited. The seasonal extremes in insolation, coupled with complex topography and urban form, demand forecasting frameworks that are both data-adaptive and climatically robust. Moreover, with evidence that rooftop solar in cities like Tromsø could meet up to 30% of local electricity demand, the importance of precise, site-specific irradiance forecasting becomes even more pronounced. Many forecasting studies in environmental sciences and renewable-energy applications focus on a small set of established learning algorithms (e.g., ANN, SVM/SVR, and Random Forest) for which performance and generalization properties have been widely discussed71. In addition, several studies report that hybridization, through data preprocessing and/or metaheuristic hyperparameter tuning, can further improve SVM/SVR performance in related forecasting tasks72,73,74 Accordingly, the present study uses SVM as a controlled base learner to systematically quantify the incremental value of (i) feature selection and lag-structure design and (ii) different metaheuristic optimizers. The aim is not to claim universal superiority over all alternative machine-learning/deep-learning methods, but to provide a transparent, like-for-like comparison within a unified modeling framework. In line with this objective, we develop and evaluate SVM-based hybrid forecasting models for daily PV power in southern Norway (Kristiansand, Stavanger, and Oslo) using PVGIS-ERA5 derived PV output and near-surface air temperature data covering 2020–2023. Four metaheuristic optimizers (ABC, GWO, GA, and PSO) are compared, and four alternative input configurations are defined via the Minimum Redundancy Maximum Relevance (MRMR) feature selection method. Model performance is assessed using multiple goodness-of-fit criteria (r, RMSE, NSE, KGE, and PI) together with visual diagnostics. The main contributions of this work are as follows: A daily PV power forecasting case study for three southern Norway sites using consistent PVGIS-ERA5 input data. Development of a controlled comparison of four metaheuristic optimizers (ABC, GWO, GA, PSO) for tuning an SVM forecasting model. Construction of four lagged input structures via MRMR and evaluation of four lagged input structures combining PV history and temperature signals. Multi-metric evaluation (r, RMSE, NSE, KGE, PI) complemented by visual diagnostics to interpret performance differences and model limitations. This work presents an analysis of PV generation prediction based on the data derived from three distinct locations of Norway. The analysis used SVM with a range of optimization techniques implemented including PSO, GWO, GA and ABC. The methodological framework in this study is summarized graphically in Fig. 1. Methodological framework. This study focuses on three coastal locations across southern Norway to evaluate solar radiation and PV power potential. The selected sites vary in latitude, elevation, and orientation. This diversification provides examination of how geographic and topographic parameters affect solar energy production. Geographically, the study area extends between 58.15° to ~ 59.90°N latitude and 5.73° to ~ 10.74°E longitude which covers a diverse range of coastal terrains. Table 1 presents the key site characteristics including geographic location, elevation above sea level, slope, azimuth, and PV system specifications considered. The PVGIS-ERA5 radiation database was used to ensure climatic input consistency across all locations (PVGIS 5.3)75. The optimal slope angles fall within 46° to 49 range and azimuth values span from − 5° to 0°. These values indicate minor deviations from due south alignment, which is considered the optimal for fixed PV installations at Northern Hemisphere latitudes. A standard monocrystalline silicon PV system with a nominal capacity of 1.0 kWp were assumed for each site. Uniform system losses of 14% are applied to account for thermal, wiring, and inverter-related inefficiencies. The selection of these representative sites and configuration aim to make the results more comparable and transferable to broader PV deployment scenarios across similar latitudes and climates. In this study, hourly PV output and temperature records were first obtained and then aggregated to a daily scale for forecasting. Hourly PV output contains structurally zero values during nighttime; if retained, these long zero sequences can dominate error metrics and encourage trivial predictions dominated by nocturnal conditions rather than the physically informative daylight signal. To focus on daytime generation, nighttime hours were excluded and the daily PV target was computed as the total PV power generated during a representative 7-hour daylight window (selected to consistently capture peak sun hours for the region and to represent daytime generation consistently across the study period). Daily mean air temperature was used as the meteorological predictor. This aggregation inevitably reduces intra-day variability; therefore, sub-daily (hourly) forecasting and uncertainty quantification are recommended directions for future work. Table 1 represents statistics of data used in this study (Fig. 2). Study locations in southern Norway.. The map was generated using ArcMap (version 10.8) from ArcGIS Desktop (https://desktop.arcgis.com). Using different model structures in analyses may either increase or decrease model performance. Numerous studies and applications related to this topic can be found in the literature. Researchers often prefer feature selection methods grounded in statistical or stochastic process theory, and MRMR is one such technique. The Minimum Redundancy Maximum Relevance (MRMR) method is an effective feature selection technique designed to identify the optimal subset of input features for predicting an output. Its primary goal is to select features that are highly relevant to the output while maintaining minimum redundancy among themselves76. By prioritizing crucial, uncorrelated features, MRMR can enhance machine learning model accuracy and significantly reduce the risk of overfitting based on a greedy algorithm and a relevance-redundancy measure, making it particularly effective for high-dimensional datasets. In this study, the MRMR method was used to define the input structure for our models, based on its preference in the literature for yielding significant results77. For more details on the method, see Ding and Peng76. Through the MRMR, lagged input structures from PV output and air temperature were constructed. The significance levels and lagged values of temperature and PV data at different time intervals. Figure 3 shows the feature importance ranking from the MRMR analysis, and the resulting input combinations are summarized in Table 2. Feature importance results from the MRMR analysis. (Pt = PV target value on day t; Pt5 = PV value lagged by 5 days; Tt4 = temperature value lagged by 4 days; etc.). Support Vector Machines (SVMs) and their regression variant SVR rely on kernel functions to map nonlinear relationships in complex datasets. The support vector machine (SVM) is a classifier that belongs to the kernel approaches in machine learning. This learning system is employed to classify and predict the data fitness function, aiming to minimize mistakes in data categorization or the fitness function itself. To advance these methods not only the optimization kernel architectures or refining hyperparameters but also tailoring implementations for particular application contexts were used78,79,80,81. Wang et al.82 demonstrated that the Gaussian kernel’s spread parameter (γ) must fall within a specific range to ensure the model achieve optimal generalization. SVM is extensively utilized for both regression and classification problems. Owing to its adaptability and efficacy it is positioned as a premier method in machine learning. For practical applications, Kusuma and Kudus83 describe SVR as a regression method designed to control overfitting and demonstrate its application on mortgage survival data using a linear kernel. Theoretical foundations connecting SVMs to probabilistic frameworks emerged through Wang et al.‘s84 work linking Gaussian kernel density estimation (GKDE) to kernel-based learning. Their analysis revealed that Gaussian-kernel SVMs operate as probabilistic classifiers, thereby providing Bayesian justification for the algorithm’s empirical success. . The mathematical formulation of SVM is presented in Eq. (1), defining the relationship between input and output variables as follows: where (:varphi:left(xright)) denotes a high dimensional feature space, w represents weight vectore, and b referred as the bias term. For implementation details and an extended reference trail used in closely related hybrid SVM studies the readers can refer to previous studies of Oruc et al.38 and Oruc et al.85 and for foundational SVR/SVM theory and formulation, cite standard SVR/SVM sources. In this study, SVM algorithm was used to predict PV. Optimization methods, which are ABC, GWO, GA, and PSO, were used to enhance model performances. Initially, algorithm learning was performed with 70%, this ratio is frequently preferred in the literature, and it has been reported by many researchers that it yields effective results, of the dataset obtained from the region. Besides, time series data was not shuffled during the analysis because the sequence of data is critically important in time series analysis. The data was separated into training and testing sets without altering this sequence. The split ratios were selected based on proportions commonly found in the literature73,77,85. No random shuffling was applied, in order to simulate real forecasting conditions. Furthermore, preliminary steps or precautions against overfitting were taken using performance metrics like PI. Then, the optimization techniques mentioned above were used to further improve the performance of these algorithms. The results obtained from these models were evaluated according to performance evaluation criteria, RMSE, r, PI, KGE and NSE. All models were trained on the training set, and their performance was evaluated on the test set as described below. Karaboga86 modeled the Artificial Bee Colony (ABC) algorithm based on honeybee foraging behavior. The algorithm divides the colony into three functional groups which exhibit distinct search behaviors. Employed bees exploit known food sources and communicate their quality through waggle dances at the hive. Onlooker bees observe these dances, evaluate potential sources based on profitability, and focus search efforts on those promising locations. Scout bees are responsible for exploring search space randomly and identifying new sources when existing sites are depleted87,88,89. The algorithm maintains a balance between exploration and exploitation through dynamic role transitions. When a particular food source fail to show improvement over successive iterations, the employed bee assigned that location abandons it and transitions into a scout bee. This conversion starts random exploration for new opportunities90. This abandonment mechanism prevents the algorithm from being trapped in suboptimal solutions. The scouts introduce diversity and novelty into the search process, employed and onlooker bees refine and extract benefit from known solutions. Mathematical details and implementation procedures provided in Karaboga86, Li et al.88, and Vitorino et al.89. Mirjalili et al.91 introduced the Grey Wolf Optimizer (GWO) by modeling the hunting dynamics observed in grey wolf packs. The algorithm transforms this behavior into a computational optimization framework. Grey wolves hunt through a hierarchical system of coordinated roles that includes tracking prey, encircling, and attacking. This is a strategy that translates effectively into computational search patterns92,93. The straightforward linear structure of the algorithm simplifies implementation while maintaining performance and has produced successful results in many fields, as mentioned by numerous researchers94. GWO establishes candidate solutions based on the observed wolf pack hierarchy. The alpha (α) represents the current best solution and positions itself as the leader of the search process. Beta (β) and delta (δ) wolves correspond to the second and third-best solutions that guide exploration of potential regions. All remaining solutions function as omega (ω) wolves that represents the pack’s lowest tier that explores the broader search space91,95. This hierarchical structure drives the optimization mechanism forward. Alphas direct the hunt, betas and deltas refine the search direction and omegas ensure diversity in exploration. Mathematically, the three hunting phases translate into iterative position adjustments. These are namely, tracking, encircling, and attacking phases which are guided by the alpha, beta, and delta solutions. Each iteration recalculates and refines wolf positions based on their distance from these leaders. The process progressively tightening the search around optimal regions until the algorithm reaches convergence, that is two criteria are determined in the GWO process: (1) catching the prey (reaching the best solution) and (2) reaching the maximum number of iterations96. Details governing the process can be found in Mirjalili et al.91. Holland introduced the Genetic Algorithm (GA) in 1960 and refined it over the following two decades. The algorithm translates Darwin’s evolutionary principles into computational optimization methods. GA maintains a population of potential solutions that compete, recombine, and randomly mutate across iterative generations. This process directly parallels biological evolution where advantageous characteristics persist through populations while disadvantageous ones disappear44 and97. Industrial applications have validated the effectiveness of the algorithm across diverse application domains, ranging from parameter optimization to complex scheduling problems46,47,98,99. The fundamental strength of the algorithm lies in its gradient-free search mechanism. This feature enables it to handle both continuous and discrete optimization challenges without requiring gradient calculations. Such a requirement constrains many traditional optimization methods44 and97. Bras et al.100 documented GA’s flexibility through applications extending from linguistic analysis to fuzzy network tuning. Their work demonstrates how the same evolutionary mechanisms, namely crossover, mutation, and selection, can be adapted to different problem domains100,101. The algorithm proceeds through three core stages. First it initializes a random population, second applies genetic operators through probabilistic rather than deterministic process, and third evaluates solution quality until convergence criteria trigger termination101,102. When solutions fail to meet established quality thresholds, the algorithm iterates through another generational cycle. This iteration systematically refines and enhances the population through selection pressure and genetic variation101,103. These steps are: Crossover (stochastic): part of two solutions “is swapped” to produce new ones. Mutation (stochastic): part of a new solution “is flipped” to generate a new one and prevent it from converging into local optima. Selection: the new solutions are evaluated according to the objective function, and the best candidates are selected. In certain instances, such as a high mutation rate that could lead to the loss of good solutions, the elitism operator is employed to guarantee that the optimal solutions are transferred to the next generation without modification, ensuring that the best candidates are maintained within the solution set100,103. Kennedy and Eberhart introduced particle swarm optimization (PSO) in 1995. This algorithm conceptually inspires from coordinated and collective animal behaviors observed in natural system such as schools of fish navigating currents and bird flocks foraging collectively. Unlike genetic algorithms and other comparable evolutionary methods, PSO achieves more rapid convergence while requiring fewer computational resources. This advantage becomes particularly evident when addressing nonlinear optimization problems and impact modeling success104,105. The algorithm treats each potential solution as a particle moving through complex search spaces and is to use information of the current position X and velocity V of particles106. Initially, particles are positioned randomly across the solution space. As the algorithm progresses through successive iterations, each particle adjusts its trajectory based on two guiding influences. These are the particle’s best position discovered so far and the globally best position identified by any particle of the entire swarm. This dual-component memory system drives particles toward potential solution regions while simultaneously maintaining exploration capability107,108,109,110,111. The search continues until particles converge on a shared optimal location. This convergence signals that the algorithm has identified the best available solution within the search space112,113,114. The correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and Performance Index (PI), were employed key statistical indicators to assess model performance as specified in Equations (2) through (6). (2), (3), (4), (5), and (6) NSE and KGE are goodness-of-fit measures that attain 1 for perfect prediction. For NSE, a value of 0 indicates performance equivalent to the mean-prediction baseline, whereas KGE uses a benchmark threshold of approximately − 0.41 for comparable interpretation115. In this study, total daily PV power generation data and daily average temperature values obtained from Kristiansand, Stavanger, and Oslo, which are in the southern region of Norway, were used to investigate the performance of forecasting models that were developed through combination of machine learning and optimization techniques. A total of 70% of the data was employed for training, while the remaining 30% was reserved for testing. Support Vector Machines (SVM) were selected as the machine learning algorithm, whereas optimization techniques included the Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) due to one of the primary objectives of the study which is to enhance model performance by hybridizing. Furthermore, in order to examine the effect of model input variables on prediction performance, the MRMR (Minimum Redundancy Maximum Relevance) method was applied to construct four different input structures. The results of all analyses are presented in Table 3. Furthermore, it should be noted that these results and rankings (e.g., PSO-M04 being best) are specific to the data and sites analyzed; generalizing beyond this case study should be done with caution. According to the results presented in Table 3, the performance metrics of the four input structures analyzed with SVM in the Kristiansand region differ from one another. Among these, the lowest performance was observed in M01, while the strongest baseline (untuned) SVM results were obtained with M04 (r = 0.6469, NSE = 0.3587, KGE = 0.3141, PI = 0.2937, and RMSE = 0.8004). The analysis of model structures shows that M01 was built using one input variable (Pt-5), while M04 was built using four input variables (Pt-5, Pt-3, Tt-4, and Tt-1). While both models used Pt as the target variable, integrating power data with temperature improves daily forecasting precision based on the results. Evidence from the analysis also reveals the success of MRMR-based input selection process in identifying superior input combinations. MRMR-based input selection process when coupled with hybridization of SVM with metaheuristic optimizers, also a consistent improvement was obtained in performance metrics (Table 3). Therefore, results also validate the value of hybridizing machine learning with optimization techniques compared to standalone configurations. However, the differences were marginal, which makes it challenging to identify a single superior optimizer for this region. Despite this, a closer look at the performance metrics revealed that SVM-PSO-M04 delivered the best test results for the Kristiansand region (r = 0.7707, NSE = 0.5748, KGE = 0.7092, PI = 0.2964, and RMSE = 0.6513). Among the tested models it can be concluded that metaheuristic hyperparameter tuning can enhance SVM performance for forecasting in this study. Compared to the analysis without any optimization technique (SVM-M04), the performance metrics were improved. Additionally, when the results of the other optimization methods were evaluated, the M04 model consistently demonstrated success across all cases. This finding further validates the effectiveness of the MRMR method in determining the model input structure for this region; while these results should be interpreted as site- and dataset-specific. Stavanger is another station within the study area where analyses were conducted. At this station, the same methods as in the previous case were applied, and the results are presented in Table 3. In the analyses performed solely with SVM, the M04 model achieved the highest scores among the four input structures (r = 0.6091, NSE = 0.3018, KGE = 0.2543, PI = 0.3548, and RMSE = 0.8352). Similar to the previous station, the input structure of M04 also demonstrated effective performance in this part of the analysis. For this station as well, optimization techniques were utilized to improve model performance. In the analyses involving ABC, GWO, GA, and PSO, performance metrics generally improved, with the results being very close to each other. Therefore, it was difficult to identify a single best-performing algorithm for the Stavanger station also. A primary takeaway of the analyses can be the overall performance among the optimization algorithms which produced comparable results. Another key observation is while the highest score metrics were obtained from M03 with the SVM-ABC and SVM-PSO techniques, all other methods achieved their highest scores using M04. These results suggest that the effectiveness of model input structures may vary upon regional environmental characteristics and they are sensitive to those characteristics. An additional finding is that using an optimal number of input variables with expanded data diversity which means balancing input complexity with representativeness, can achieve higher predictive precision. Another station from which data were collected within the study area is Oslo. All of these stations are located in the southern region of Norway, where sunshine duration is considerably higher compared to other regions located in the country. The analysis results obtained from this station are presented in Table 3. As in the other stations, analyses were first performed using only SVM, and optimization techniques were subsequently applied to improve the results. Overall, the analyses incorporating optimization techniques improved the performance metrics of all models. In the analyses performed solely with SVM, the highest scores were obtained with M04 (r = 0.5994, NSE = 0.3043, KGE = 0.2518, PI = 0.3344, and RMSE = 0.8336). Among the optimization-based analyses, SVM-PSO-M04 achieved the highest test scores (r = 0.6861, NSE = 0.4470, KGE = 0.6252, PI = 0.3686, and RMSE = 0.7428), although differences among the top optimizers were modest. Unlike the Stavanger station, the best-performing input structure at this station was obtained with M04. Therefore, it can be concluded that while determining model input structures, not only PV power data but also temperature information can be beneficial for daily PV forecasting. In general, across all regions, in this dataset, the strongest results were most often obtained with M04, while the input structure M01 exhibited the lowest performance metrics. All optimization techniques improved the performance of the models relative to the untuned SVM baseline. However, because performance differences among optimizers were relatively small, PSO should be interpreted as yielding the highest scores in this study rather than as universally superior. In this study, in addition to statistical calculations, visual comparison methods were also employed. For the three different stations within the study area, violin plots were generated and are presented in Fig. 4. These plots were constructed to enable the comparison of the best-performing models within each category. According to the results, the only model that did not visually align with the observed values for Kristiansand was SVM-M04. Almost all of the other plots displayed highly similar distributions. Therefore, the most critical aspects to consider in this context are measures such as the mean, extremes, and median. Violin diagram for all models in different stations on study area where SVM-M04; analysis of baseline SVM with M04 and, SVM-ABC-M04; analysis of SVM coupled ABC (hybridized SVM) with M04 etc. (a) Kristiansand (b) Stavanger (c) Oslo. Although it is rather difficult to distinguish between the models based on visual observations alone, an evaluation of kernel densities, the 3rd quartile, and median values revealed that the model most similar to the observed data was SVM-PSO-M04. In Stavanger, the best-performing model was identified as SVM-PSO-M03, while in Oslo, the most successful model was again SVM-PSO-M04. In determining these successful models, multiple statistical parameters from the violin plots were taken into consideration. In Fig. 5, a box-normal plot is presented to enable a better comparison of the best-performing models. Upon detailed examination of this plot, it appears quite difficult to distinguish the differences between the observed values and the prediction models. This difficulty mainly arises from the fact that all optimization methods produced results that were very close to each other. While the prediction models in the Kristiansand and Stavanger regions yielded highly similar outcomes, a few of the prediction models in the Oslo region could be distinguished more easily. In this region, the SVM-ABC-M03 and SVM-GWO-M04 models visually resembled the observed values more closely than the others; however, in terms of statistical results, these two methods lagged behind. Thus, the visual outcomes and the statistical results did not fully align. In conclusion, when the performance metrics obtained from prediction models are very close to one another, the box-normal method should not be preferred for visual comparison. Box-normal diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. (a) Kristiansand (b) Stavanger (c) Oslo. The ridge plot, another visual comparison method, is also included in this study. Figure 6 shows the ridge plot for all models that were successful in their respective class. Upon examining this plot, a model that visually overlaps with the observation values in the Kristiansand and Oslo regions could not be identified. However, in the Stavanger region, the models were able to predict the peak that occurred in the initial values of the observation data. But this was also not effective in determining the most successful model. Consequently, this visual comparison method cannot be considered successful either. In conclusion, the ridge plot was also unsuccessful in determining the best model, that is, in comparing prediction models whose performance metrics were very close to each other. Ridge diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. (a) Kristiansand (b) Stavanger (c) Oslo. Apart from the Violin plot, the visual comparison methods mentioned above did not yield exceptional results in distinguishing the most successful models. Therefore, to both increase the comparison comprehensiveness of the study and to enable a more qualified distinction of the results, Bland-Altman and Box plots were generated for all regions. All plots, specific to different regions, are shown in Figs. 7 and 8, and 9. Bland-Altman diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. in Kristiansand. In Fig. 7, the differences between the prediction models based on the observed values at 95% limits of agreement level are shown. One of the most important results here is that the differences for the SVM-PSO-M04 model are notably clustered around the zero-line indicating little bias for the SVM-PSO-M04 model. Although the predicted values for the other models also concentrate around zero, the densest region was determined to be SVM-PSO-M04. Therefore, this is an indication that it is the most successful model in that region. Bland-Altman diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. in Stavanger. Figure 8, on the other hand, shows the differences between the prediction models for Stavanger at 95% limits of agreement level, based on the observed values. The key point is that the predicted values for all models failed to cluster around the zero line, meaning their predictions have larger deviations from observations. Therefore, this indicates that no conclusive results were found for Stavanger. However, when looking at the SVM-PSO-M03 model, the predicted values are clustered between the 95% limits of agreement, which suggests that results close to the observed values were obtained. The predicted values are more concentrated between these two lines compared to the other models. Bland-Altman diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. in Oslo. Figure 9 displays the Bland-Altman plots for the prediction models based on the observed values for the Oslo station. Upon examining the figure, it is observed that the differences in the predicted values for the SVM-GA-M03 and SVM-PSO-M04 models are concentrated around zero. This indicates that these models yield more effective results compared to the others. The results here are consistent with the statistical findings. To ensure the reliability of the results obtained in this study, ANOVA (Analysis of Variance) and Kruskal-Wallis statistical tests were applied to the forecasting models developed for the Stavanger, Kristiansand, and Oslo regions. The findings were examined at a 95% significance level in Table 4. For all stations, p-values were greater than 0.05, indicating that we fail to reject the null hypothesis of no statistically significant differences among the compared groups at 95% confidence. This suggests that the performance differences among the best models are modest and should be interpreted alongside the multi-metric evaluation and visual diagnostics. Also supporting the claim that while PSO-M04 had the highest metrics in general, its edge was not statistically significant. The use of machine learning and optimization techniques in a hybridized manner is among the methods preferred by researchers across many disciplines in literature. Model performance metrics are typically improved using optimization techniques. In this study, consistent but moderate improvements in model performance values were also achieved through optimization techniques. The results obtained in this study, both in terms of the methods used and the optimization techniques applied, are consistent with those of several studies in literature. Some of these include: AlMohimeed et al.116 developed prediction models for the forward-looking estimation of cancer cells by utilizing image processing methods. The SVM-PSO model achieved one of the successful results. Just as this hybrid method was effective in the forward-looking prediction of cancer cells, it has also proven effective in the early diagnosis of hypertension problems117. In addition to their use in the health sector, hybrid methods are also utilized in hydrological studies. Oruc et al.38 investigated the performance of forward-looking drought prediction models in the Norwegian region by utilizing The Adaptive Neuro-Fuzzy Inference System (ANFIS) and SVM. They improved the model performances by hybridizing the machine learning algorithms with GWO, ABC, PSO, and GA. In their results, they emphasized that effective outcomes were obtained in analyses using SVM-PSO. In addition to these, they created 12 different model input structures using cross-correlation. In their conclusions, they stated that the data type and the number of delayed data points in the model input structure should be kept at an optimum level. Results consistent with these findings were also obtained in this study. To broaden the scope of the study, the model input structure in this work was created using the MRMR method instead of cross-correlation. Another study that obtained effective results with SVM-PSO is that of Samantaray et al.118. In their study, they chose Back Propagation Neural Network (BPNN) and SVM, along with PSO from the optimization techniques. Samantaray et al., who aimed to model the forward-looking prediction of floods in the Barak valley by creating 5 different input structures, obtained the most effective results from analyses performed with SVM-PSO. Unlike this study, they did not use any delayed data in their model input structures. Furthermore, they enriched their model input structure with meteorological data. In the results of their work, they also mentioned that effective results were achieved through this enrichment. In many studies in the literature where hybrid machine learning and optimization techniques are preferred, usually only two or three optimization techniques are used. In this study, however, SVM, which is considered a well-established machine learning algorithm, was chosen and hybridized with four different optimization techniques (ABC, GWO, GA, and PSO). While these optimizers have been widely applied across different domains, side-by-side comparisons within a unified high-latitude PV forecasting setup remain relatively limited. Although the combined use of these methods strengthens the comparative aspect of our study, including additional machine learning models (e.g., a neural network or ensemble method) could further broaden the scope of comparison. However, such analyses involving optimization techniques are highly time-consuming and computationally expensive. This would move beyond the scope of this study and potentially become a topic for a separate follow-up work. Researchers interested in hybrid models may consider comparing a small set of learners and optimizers under consistent validation protocols and may also investigate sensitivity to learning rates and hyperparameter ranges. The use of hybrid methodologies has become widespread across many disciplines today. Recently, deep learning techniques such as CNN, and CNN-LSTM have gained increasing visibility in research. These methods are considered highly innovative due to their nature as modifications of ANN architecture119. However, in this study, a key motivation was to benchmark the extent to which an established method (SVM) can benefit from input-structure design and metaheuristic tuning in a high-latitude PV forecasting context before moving to more complex deep-learning approaches. Deep-learning methods remain a promising direction for future work. The models utilized in this study are data-driven models. Although data-driven models have advanced beyond physical-based models, the performance comparison between the two modeling approaches is frequently debated among researchers. This debate arises from the fact that physical-based models incorporate multiple parameters that accurately portray real-life phenomena. In contrast data-driven models can be very powerful but they also rely on heavily the quality and quantity of available data and sometimes may not capture certain physical constraints. Therefore, for critical applications, both approaches should be used to cross-validate results and ensure robustness for the area being examined. Limitations of this study include: (i) daily aggregation of PV output, which smooths intra-day dynamics and ramp events; (ii) the use of a fixed 7-hour daytime window, which may not fully represent seasonal daylight variability at high latitudes; (iii) reliance on PVGIS-ERA5 derived PV output rather than site-measured power, which can introduce modeling biases; (iv) a restricted predictor set (primarily temperature and lagged PV) without additional meteorological drivers (e.g., irradiance, cloud cover, wind); (v) a limited number of sites and years (2020–2023); and (vi) evaluation based on a single chronological split without rolling-origin cross-validation or probabilistic forecasts. These limitations restrict generalization beyond the studied sites and period. Future work should (i) use measured PV data when available, (ii) expand predictors, (iii) benchmark against additional ML/DL baselines (e.g., RF, gradient boosting, LSTM/GRU, CNN-LSTM), (iv) apply rolling-origin evaluation, and (v) report prediction intervals to quantify forecast uncertainty. Addressing these limitations would allow to generalize the findings. This study developed SVM-based hybrid models tuned with ABC, GWO, GA, and PSO to forecast daily PV power for three southern Norway locations (Kristiansand, Stavanger, and Oslo) using PVGIS-ERA5 derived PV output and temperature data covering 2020–2023. A chronological 70/30 train–test split was used to evaluate out-of-sample performance. Across all sites and input configurations, metaheuristic tuning improved test performance relative to the untuned SVM baseline. PSO produced the best mean test metrics, although differences among the top-performing optimizers were generally small. Input configurations that included both lagged PV power and lagged temperature (M03–M04) typically outperformed PV-only structures (M01–M02), confirming the value of incorporating meteorological inputs in PV forecasting. Model skill varied by location, with the highest scores obtained for Kristiansand in this dataset. These results should be interpreted as case-study findings for southern Norway rather than universal performance rankings. Visual error analysis (e.g., Bland–Altman plots) plots were particularly helpful for diagnosing bias and agreement between observations and predictions; however, when model metrics are very close, visual methods alone may be insufficient to clearly distinguish models. However, this study’s scope is limited to daily data from three sites and a specific train-test split; thus, the results should be interpreted as a case study rather than generalized truths. The use of simulated PVGIS data, a restricted set of input features, and absence of cross-validation can be list as key limitations in this study and will be addressed in future work. Overall, the proposed SVM–metaheuristic framework provides a benchmark for high-latitude PV power forecasting. It demonstrates that even established machine learning models can benefit significantly from intelligent input selection and parameter tuning. Future studies can build on this by incorporating additional data sources (e.g., irradiation and cloud cover measurements) and exploring advanced models to further improve forecast reliability. The hyper-parameters used for each technique are shown in Table 5. The original contributions presented in the study are included in the article. The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request. Bosetti, V., Catenacci, M., Fiorese, G. & Verdolini, E. 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The Center for Sámi Studies, UiT Norges Arktiske Universitet, Tromsø, 9037, Norway Sertaç Oruç Civil Engineering Department, Faculty of Engineering and Natural Sciences, Ankara Yıldırım Beyazıt University, 15 Temmuz Şehitleri Campus, Ankara, 06010, Turkey Sertaç Oruç Civil Engineering Department, Faculty of Engineering, Central Campus, Aksaray University, Aksaray, 68100, Türkiye, Turkey Mehmet Ali Hınıs Civil Engineering Department, Technology Faculty, Central Campus, Gazi University, Ankara, 06560, Turkey Türker Tuğrul Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Conceptualization, S.O. and M.A.H.; Formal analysis, T.T.; Methodology, T.T., S.O. and M.A.H.; Supervision, S.O. and M.A.H.; Visualization, T.T., S.O. and M.A.H.; Writing—original draft, T.T. and S.O.; Writing—review and editing, T.T., S.O., and M.A.H. All authors have read and agreed to the published version of the manuscript. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Oruç, S., Hınıs, M.A. & Tuğrul, T. Forecasting photovoltaic power in high-latitude regions via support vector machine optimized by meta-heuristics. Sci Rep16, 3438 (2026). https://doi.org/10.1038/s41598-025-33415-7 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-025-33415-7 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Subscribe For more great content Subscribe For more great content A 13.94-acre RV and boat storage facility proposed for southwest Manteca will have 12 canopies with solar panels. Gateway Solar RV & Boat Storage will have the ability to generate 3,000 kilowatts of electricity. That’s enough to power 400 average California homes. The project is being built at the western end of Bronzan Road bordering the Oakwood Shores gated community on the west and the 120 Bypass on the north. Bronzan Road is the first street that intersects with McKinley Avenue immediately south of its interchange with the 120 Bypass. It is being reviewed by the Manteca Planning Commission on Thursday at 6 p.m. The solar canopies will cover 299,884 square feet, an area just slightly larger that double the size of the Manteca Costco store. There will also be a 2,326 square foot office building. It will be Manteca’s second RV and boat storage facility with protective canopies with solar panels on top. Manteca Executive RV & Boat Storage opened last year in southeast Manteca on the northeast corner of Atherton Drive and Woodward Avenue. The complex can accommodate 532 recreational vehicles. The overall complex covers 13.68 acres.
It is a full service storage facility with a 2,400-square-foot office building, vehicle cleaning station, vehicle dumping station, plus propane and air filling stations, and even an ice machine. That complex also has roughly 300,000 square feet of solar arrays. As it stands now, it is the largest solar array in one location in Manteca. Between the two projects, they will be able to store more than 1,000 boats and RCs. An 884-unit storage complex is now being built in southwest Manteca within a mile of the Bronzan Road project on 5.64 acres abutting Bella Vista Drive with a 2,524-square-foot office building.
To contact Dennis Wyatt, email dwyatt@mantecabulletin.com
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First Solar, Inc. (FSLR – Free Report) focuses primarily on the design, production and sale of advanced thin-film cadmium telluride (CdTe) photovoltaic modules. It is one of the largest global manufacturers of thin-film solar modules and the leading U.S.-headquartered producer in this segment, with manufacturing facilities located in the United States, India, Malaysia and Vietnam.
The company’s product portfolio includes its Series 6 Plus and Series 7 modules, which are largely supplied to utility-scale developers, independent power producers, and large commercial and industrial customers. The Series 7 modules feature a larger form factor and are produced through an integrated manufacturing platform intended to streamline installation and support cost efficiency.
First Solar operates a vertically integrated production process that allows it to oversee semiconductor fabrication, module assembly and quality assurance within its facilities. This structure supports operational consistency, throughput management and cost control on a per-watt basis.
FSLR continues to increase its domestic manufacturing capacity, supported by U.S. clean energy policies and steady demand for utility-scale solar installations. A meaningful portion of its revenues is generated in the United States, where interest in domestically produced modules remains stable. At the same time, First Solar maintains ongoing investments in research and development, including initiatives focused on next-generation thin-film and tandem technologies, aimed at improving module efficiency and supporting long-term competitiveness. Manufacturing investments and policy support are strengthening scale and cost efficiency in the solar module segment. Other solar module manufacturers operating under similar investment and demand trends are discussed below.
Canadian Solar Inc. (CSIQ – Free Report) continues to invest in expanding and optimizing its global manufacturing footprint, supporting the production and supply of crystalline silicon solar modules to utility-scale and commercial customers worldwide.
JinkoSolar Holding Co. Ltd. (JKS – Free Report) is prioritizing manufacturing capacity enhancements and operational improvements, reinforcing its position as a major global supplier of crystalline silicon solar modules across international markets. The Zacks Consensus Estimate for 2026 earnings per share indicates an increase of 58.82% year over year. FSLR’s long-term (three to five years) earnings growth rate is 33.5%. Image Source: Zacks Investment Research First Solar is trading at a discount relative to the industry, with a forward 12-month price-to-earnings of 9.22X compared with the industry average of 17.4X. Image Source: Zacks Investment Research In the past six months, FSLR shares have risen 22.4% compared with the industry’s 43.8% growth. Image Source: Zacks Investment Research First Solar currently has a Zacks Rank #3 (Hold). You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here. JinkoSolar Holding Company Limited (JKS) – free report >> First Solar, Inc. (FSLR) – free report >> Canadian Solar Inc. (CSIQ) – free report >> Our experts picked 7 Zacks Rank #1 Strong Buy stocks with the best chance to skyrocket within the next 30-90 days. Recent stocks from this report have soared up to +97.3% within 30 days – this month’s picks could be even better. See our report’s fresh new picks today – it’s really free! Privacy Policy | No cost, no credit card, no further obligation. Past performance is no guarantee of future results. This page has not been authorized, sponsored, or otherwise approved or endorsed by the companies represented herein. Each of the company logos represented herein are trademarks of Microsoft Corporation; Dow Jones & Company; Nasdaq, Inc.; Forbes Media, LLC; Investor's Business Daily, Inc.; and Morningstar, Inc. Copyright 2026 Zacks Investment Research | 101 N Wacker Drive, Floor 15, Chicago, IL 60606 At the center of everything we do is a strong commitment to independent research and sharing its profitable discoveries with investors. This dedication to giving investors a trading advantage led to the creation of our proven Zacks Rank stock-rating system. Since 1988 it has more than doubled the S&P 500 with an average gain of +23.86% per year. These returns cover a period from January 1, 1988 through February 2, 2026. Zacks Rank stock-rating system returns are computed monthly based on the beginning of the month and end of the month Zacks Rank stock prices plus any dividends received during that particular month. A simple, equally-weighted average return of all Zacks Rank stocks is calculated to determine the monthly return. The monthly returns are then compounded to arrive at the annual return. Only Zacks Rank stocks included in Zacks hypothetical portfolios at the beginning of each month are included in the return calculations. Zacks Ranks stocks can, and often do, change throughout the month. Certain Zacks Rank stocks for which no month-end price was available, pricing information was not collected, or for certain other reasons have been excluded from these return calculations. Zacks may license the Zacks Mutual Fund rating provided herein to third parties, including but not limited to the issuer. Visit Performance Disclosure for information about the performance numbers displayed above. Visit www.zacksdata.com to get our data and content for your mobile app or website. Real time prices by BATS. Delayed quotes by Sungard. NYSE and AMEX data is at least 20 minutes delayed. NASDAQ data is at least 15 minutes delayed. This site is protected by reCAPTCHA and the Google Privacy Policy, DMCA Policy and Terms of Service apply.
Support WBUR It’s climate report card time in Massachusetts. The Healey administration has published its annual assessment of the state’s progress in meeting its legally-binding clean energy and climate goals. The results, which look at 2025, are mixed. There are some bright spots, like higher than expected heat pump installations. And there are some areas of concern, like weaker than expected electric vehicle adoption. But the report shows progress is happening, even as the Trump administration makes it harder and more expensive for Massachusetts to do things like reduce planet-warming emissions, adapt to increased flooding and extreme heat, and permanently protect forests and farm lands. “2025 has been a tough year for climate action,” said Katherine Antos, undersecretary of decarbonization and resilience at the Massachusetts Executive Office of Energy and Environmental Affairs. “Between the early termination of federal tax credits and the unlawful cancellation of grants, losing a federal partner has real impacts on Massachusetts, and it is delaying our progress in some key areas.” Still, she added, it’s not all bad news. “Massachusetts is doing very well on the areas where we can control.” She cited the completion of a new high-voltage transmission line that brings clean energy from Canada into the commonwealth, and ongoing work to make sure that lower-income and minority communities don’t continue to be disproportionately burdened by energy infrastructure and pollution. Here’s a more detailed breakdown of how the state is doing in the six categories mentioned in the report card: Fossil fuel-powered cars, trucks, buses and other vehicles are the single biggest source of climate pollution in Massachusetts, and the state’s primary plan for reducing these emissions is to replace internal combustion engine vehicles with electric ones In 2025, people in the state purchased just over 27,000 electric vehicles. This brings the total number of EVs on the road to 166,296 — short of the state’s 200,000 EV target. The state attributes the slow-down in EV sales to the federal government, primarily its decision to scrap the $7,500 EV tax credit established under President Biden. Looking forward, the Massachusetts Climate and Clean Energy plan calls for 900,000 EVs on the road by 2030. Getting people to buy EVs requires a robust fleet of publicly available electric chargers. At the end of 2023, there were 6,767 public charging ports. Two years later, that number rose to 10,387. While that does represent more than a 50% increase, the pace of installment is not fast enough. The state’s plans called for 12,000 public charging ports by the end of 2025, and 45,500 by 2030. While Massachusetts can’t make up for the lost federal incentives for EV purchases, the state’s EV incentive program, MOR-EV, still exists. The Healey administration said it’s looking for ways to more effectively administer this program, and to encourage vehicle fleet owners to make the electric shift. On the charging front, the state is doubling down on its commitment to build out the network with a $46 million investment. It’s also working to get more chargers along the highway with the money it received through the federal National Electric Vehicle Infrastructure program. The Healey administration also said it will continue investing in public transportation so fewer people have to rely on private cars to get around. According the report card, MBTA ridership was up 10% from October 2024 to October 2025. Buildings may not be the first thing that comes to mind when you think about climate pollution, but the state’s reliance on natural gas and home heating oil makes housing the second biggest source of annual emissions. To make buildings more climate friendly, the state’s plans call for more electric appliances like heat pumps, and improving energy efficiency with more insulation and better windows. The climate report card shows Massachusetts is ahead of the game with annual heat pump installations. The state’s plans calls for converting 100,000 homes to heat pumps between 2020-2025, and Massachusetts ended last year with a total of 133,753 heat pump-heated homes. But state data shows that only a small percentage of the people low-income residents opted for a heat pump in 2025, a fact almost certainly attributable to the high upfront cost to buy and install the systems. Of the 30,567 heat pump systems installed in 2025, just under 4,000 of them were in low-income households. Mass Save, the state’s energy efficiency program, conducted at least 85,000 home energy audits and completed more than 50,000 residential “weatherization projects” — i.e., things that make a house more energy efficient like adding insulation, replacing old windows and installing more efficient appliances. That may sound like a lot of work, but it’s a bit less than the program did in 2024. State officials said this year-over-year decline is temporary and is likely a result of some uncertainty early in the year as the state assessed the program’s new three-year plan. Installing heat pumps and weatherizing buildings is expensive work, especially in older homes and apartment buildings. And there’s less help available to Massachusetts residents after Congress prematurely ended the home energy tax credits established under President Biden. The Healey administration said it can’t make up this gap, but it’s working to improve and streamline Mass Save to contain costs and expand its reach. Last year also saw the roll out of a reduced seasonal heat pump rate on energy bills, which the administration hopes will incentivize more people to make the switch. Finally, as outlined in the report card, the state is starting to track emissions from large buildings — think giant warehouses and skyscrapers — and plans to launch a new program this year to help them electrify. Though these structures only represent about 2% of the building stock in the state, they’re responsible for 40% of the sector’s annual emissions and energy use. For Massachusetts to meet its legally binding mid-century climate goals, it needs more of its electricity to come from renewables like wind and solar, as well as other carbon-free sources like hydropower and nuclear. But after four years of a federal administration that backed these efforts, Massachusetts now finds itself facing a White House with a very different energy vision. “ This is another area where you see the impact of federal actions,” said Antos of the state’s energy and environmental affairs department. “ But this is also an area where Massachusetts is absolutely pressing forward.” Despite ongoing attacks on the offshore wind industry, Vineyard Wind, the state’s first large-scale offshore wind project, sent a lot of power to the grid last year. The remaining construction for the project should be completed next month. The state also brought the New England Clean Energy transmission line online — a feat in and of itself, given the decade-long political battle over its construction. More than 50% of the electricity consumed in Massachusetts now comes from carbon-free sources, up from about 48% two years ago. And with the addition of Vineyard Wind and the Clean Energy Connect, Antos projects it will be closer to 75% by the end of 2026. Massachusetts’ clean energy plan calls for 3,600 megawatts of onshore and offshore wind power by 2025. The latest figures in the climate report card come from 2024, and show that the state had only 105 megawatts. Vineyard Wind, which can produce up to 800 megawatts, will add a large new source of wind power, though the state will still fall short of its goal. As for solar, the climate plan calls for 4,470 megawatts of installed capacity by 2025. By the end of 2024, the state had 3,939 megawatts. The Healey administration has said it’s committed to an “all of the above” energy strategy, which means its going hard on renewables, but also supports increasing natural gas supplies into the state to help meet growing power demands. There is tremendous uncertainty in the offshore wind industry, and it’s not clear when Massachusetts might be able to bring a second large-scale project online. In the meantime, the state is looking north to Canada as a potential source of offshore wind power. Massachusetts doesn’t expect to completely eliminate greenhouse gas emissions by 2050, so it’s relying on its forests, marshes and farm land to help suck carbon from the air and store it in the ground. To maximize this strategy, the state projects that it needs to permanently conserve a lot of its so-called “natural and working lands.” In 2025, it met its target, despite losing significant federal funding to help it do so. According to the report card, more than 28% of the state — about 1.4 million acres — is permanently protected. The report card also shows progress in tree planting, which can have a variety of public health benefits beyond absorbing carbon dioxide. Massachusetts has its work cut out for it when it comes to conservation, in large part because this work isn’t cheap and there are competing demands for undeveloped land. In 2025, Gov. Healey filed a bill known as the Mass Ready Act, which would authorize the state to spend more money on land conservation and related restoration work. Steve Long of The Nature Conservancy said this legislation is a good start, but that the state also needs to provide more incentives for private landowners to permanently conserve forests. According to Long, 80% of the state’s forested land is privately owned. It’s a lot easier to count the number of EVs on the road than it is to measure how the state’s doing when it comes to easing infrastructure and pollution burdens on low-income and minority communities. But Antos from the state said the Healey administration is committed to finding ways to track its progress on environmental justice and equity, while also furthering those goals. To that end, the state is looking at where its grant money goes and the number of resiliency projects being done in collaboration with Tribal nations. Another metric the state is looking at is “energy burden.” A household is said to be energy burdened when more than 6% of its total income goes to energy bills. In 2024, 48.9% of households in the state receiving food assistance — a proxy for low income — were energy burdened. The state is working to develop goals for reducing this burden. As the Trump administration rolled back funding and other support for all sorts of equity-related programs and projects in 2025, the report card spells out the ways the Healey administration tried to make progress on environmental justice. Some of these actions include efforts to lower utility bills, new guidelines for community involvement in project development and trying to get more people engaged in state processes. Between drought, heat waves and flooding, the effects of climate change have already arrived in Massachusetts. It’s not enough to just reduce emissions, the state also needs to find way to adapt to the changing climate. In 2025, the state made progress on several resilience efforts, such as supporting a growing number of cities and towns in drafting climate adaptation plans, despite losing funds from a key Federal Emergency Management Agency program. “Without a question we are much worse off without the FEMA Building Resilient Infrastructure and Communities program,” said Julie Wormser, Cambridge’s Chief Climate Officer. “But we never could have even applied for these big federal grants if the state funding weren’t there to get us off the ground.” Miriam Wasser is a reporter with WBUR's climate and environment team. Support WBUR Support WBUR
A solar power farm is being considered for the local airport, according to a representative from Venergy Group LLC. By JOE NAPSHA TribLive Arnold Palmer Regional Airport in Unity could become the first Pennsylvania airport of its size to install a solar power array and generate revenue. Solar power company representatives touted the proposal this week as a money-saver and revenue generator. Palmer Regional and Rostraver airports could be the sites of a $28 million to $30 million solar installation that has the capability to generate close to 6.8 million kilowatt hours of power for Palmer and a solar power farm at the Rostraver Airport. Power produced at the Rostraver site would go only to the power transmission grid. The projections were offered to the Westmoreland County Airport Authority Tuesday by Corey Harper, airport business developer for Venergy Group LLC. Airport authority members said they would consider at the board’s March 10 meeting whether to sign a letter of intent with Venergy Group to continue developing the program. That consent would cost the authority 1% of the project — about $300,000 to $360,000 for the more expansive version — if the authority later drops the plan. That solar panel array would cover about 650 parking spaces at Palmer Regional and generate 100% of the power for the airport, Harper said. A scaled-down version would generate about 5.2 million kilowatt hours of power and cost up to $20 million to place panels above 250 short-term parking spots at Palmer Regional and ground-mounted panels at the Rostraver installation adjacent to the runway. That 20-acre section of authority property lies in Allegheny County. Venergy projects that the larger solar power array would save the airport au- thority $1.7 million by reducing the need to buy power. Even with the solar panels producing power, the airport would not disconnect from West Penn Power’s electric lines, Harper said. Solar at other sites While the Pittsburgh and Philadelphia international airports have solar projects, there are none in regional airports the size of Palmer Regional, Harper said. The company is in discussions with about five other airports in the state, but he declined to identify them. One of those is the John Murtha Johnstown Cambria County Airport in Richland Township, which is considering a solar power project from Venergy to offset the airport’s electricity costs, said Cory Cree, airport manager. No decision has been made on that proposed solar project, Cree said. Harper said the company has been involved in 68 solar power projects at airports across the nation. Venergy wants the authority to sign a letter of intent to proceed, Harper said. Venergy needs a contract with the authority by June in order to prepare the design, seek government approvals and contract for construction to have it producing power by 2027. That is the deadline for obtaining solar power tax credits from the federal government, which can be worth up to half the cost of the project. The tax credits could be used to help pay for the project because some airlines might be willing to buy them to offset carbon emissions from burning jet fuel, Harper said. Paul Whittaker, airport authority chairman, said he is interested in the project. The cost for continuing development would be included in the total price if the authority proceeds. The authority needs a month to consider the project and the costs before it signs a letter of intent, said Ed Kilkeary, airport authority member. Vince Finoli, board member, said he would be in favor of signing a letter of intent only if there is funding available to cover the 1% fee. Authority members also were concerned about the total cost of a $30 million project. That larger scale project would require the authority to issue bonds worth some $30 million, and the authority does not know if it could borrow that much money, Finoli said. “Our big concern is where are we going to get that money,” Kilkeary said.
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