Scotland approves 99 MW windfarm

AI Load Forecasting for Utilities leverages machine learning, smart meters, and predictive analytics to balance energy demand during COVID-19 disruptions, optimize grid reliability, support demand response, and stabilize rates for residential and commercial customers.

Key Points

AI predicts utility demand with ML and smart meters to improve reliability and reduce costs.

✅ Adapts to rapid demand shifts with accurate short term forecasts

✅ Optimizes demand response and distributed energy resources

✅ Reduces outages risk while lowering procurement and operating costs

The spread of the novel coronavirus that causes COVID-19 has prompted state and local governments around the U.S. to institute shelter-in-place orders and business closures. As millions suddenly find themselves confined to their homes, the shift has strained not only internet service providers, streaming platforms, and online retailers, but the utilities supplying power to the nation’s electrical grid, which face longer, more frequent outages as well.

U.S. electricity use on March 27, 2020 was 3% lower than it was on March 27, 2019, a loss of about three years of sales growth. Peter Fox-Penner, director of the Boston University Institute for Sustainable Energy, asserted in a recent op-ed that utility revenues will suffer because providers are halting shutoffs and deferring rate increases. Moreover, according to research firm Wood Mackenzie, the rise in household electricity demand won’t offset reduced business electricity demand, mainly because residential demand makes up just 40% of the total demand across North America.

Some utilities are employing AI and machine learning for the energy transition to address the windfalls and fluctuations in energy usage resulting from COVID-19. Precise load forecasting could ensure that operations aren’t interrupted in the coming months, thereby preventing blackouts and brownouts. And they might also bolster the efficiency of utilities’ internal processes, leading to reduced prices and improved service long after the pandemic ends.

Innowatts
Innowatts, a startup developing an automated toolkit for energy monitoring and management, counts several major U.S. utility companies among its customers, including Portland General Electric, Gexa Energy, Avangrid, Arizona Public Service Electric, WGL, and Mega Energy. Its eUtility platform ingests data from over 34 million smart energy meters across 21 million customers in more than 13 regional energy markets, while its machine learning algorithms analyze the data to forecast short- and long-term loads, variances, weather sensitivity, and more.

Beyond these table-stakes predictions, Innowatts helps evaluate the effects of different rate configurations by mapping utilities’ rate structures against disaggregated cost models. It also produces cost curves for each customer that reveal the margin impacts on the wider business, and it validates the yield of products and cost of customer acquisition with models that learn the relationships between marketing efforts and customer behaviors (like real-time load).

Innowwatts told VentureBeat that it observed “dramatic” shifts in energy usage between the first and fourth weeks of March. In the Northeast, “non-essential” retailers like salons, clothing shops, and dry cleaners were using only 35% as much energy toward the end of the month (after shelter-in-place orders were enacted) versus the beginning of the month, while restaurants (excepting pizza chains) were using only 28%. In Texas, conversely, storage facilities were using 142% as much energy in the fourth week compared with the first.

Innowatts says that throughout these usage surges and declines, its clients took advantage of AI-based load forecasting to learn from short-term shocks and make timely adjustments. Within three days of shelter-in-place orders, the company said, its forecasting models were able to learn new consumption patterns and produce accurate forecasts, accounting for real-time changes.

Innowatts CEO Sid Sachdeva believes that if utility companies had not leveraged machine learning models, demand forecasts in mid-March would have seen variances of 10-20%, significantly impacting operations.

“During these turbulent times, AI-based load forecasting gives energy providers the ability to … develop informed, data-driven strategies for future success,” Sachdeva told VentureBeat. “With utilities and energy retailers seeing a once-in-a-lifetime 30%-plus drop in commercial energy consumption, accurate forecasting has never been more important. Without AI tools, utilities would see their forecasts swing wildly, leading to inaccuracies of 20% or more, placing an enormous strain on their operations and ultimately driving up costs for businesses and consumers.”

Autogrid
Autogrid works with over 50 customers in 10 countries — including Energy Australia, Florida Power & Light, and Southern California Edison — to deliver AI-informed power usage insights. Its platform makes 10 million predictions every 10 minutes and optimizes over 50 megawatts of power, which is enough to supply the average suburb.

Flex, the company’s flagship product, predicts and controls tens of thousands of energy resources from millions of customers by ingesting, storing, and managing petabytes of data from trillions of endpoints. Using a combination of data science, machine learning, and network optimization algorithms, Flex models both physics and customer behavior, automatically anticipating and adjusting for supply and demand patterns through virtual power plants that coordinate distributed assets.

Autogrid also offers a fully managed solution for integrating and utilizing end-customer installations of grid batteries and microgrids. Like Flex, it automatically aggregates, forecasts, and optimizes capacity from assets at sub-stations and transformers, reacting to distribution management needs while providing capacity to avoid capital investments in system upgrades.

Autogrid CEO Dr. Amit Narayan told VentureBeat that the COVID-19 crisis has heavily shifted daily power distribution in California, where it’s having a “significant” downward impact on hourly prices in the energy market. He says that Autogrid has also heard from customers about transformer failures in some regions due to overloaded circuits, which he expects will become a problem in heavily residential and saturated load areas during the summer months (as utilities prepare for blackouts across the U.S. when air conditioning usage goes up).

“In California, [as you’ll recall], more than a million residents faced wildfire prevention-related outages in PG&E territory in 2019,” Narayan said, referring to the controversial planned outages orchestrated by Pacific Gas & Electric last summer. “The demand continues to be high in 2020 in spite of the COVID-19 crisis, as residents prepare to keep the lights on and brace for a similar situation this summer. If a 2019 repeat happens again, it will be even more devastating, given the health crisis and difficulty in buying groceries.”

AI making a difference
AI and machine learning isn’t a silver bullet for the power grid — even with predictive tools at their disposal, utilities are beholden to a tumultuous demand curve and to mounting climate risks across the grid. But providers say they see evidence the tools are already helping to prevent the worst of the pandemic’s effects — chiefly by enabling them to better adjust to shifted daily and weekly power load profiles.

“The societal impact [of the pandemic] will continue to be felt — people may continue working remotely instead of going into the office, they may alter their commute times to avoid rush hour crowds, or may look to alternative modes of transportation,” Schneider Electric chief innovation officer Emmanuel Lagarrigue told VentureBeat. “All of this will impact the daily load curve, and that is where AI and automation can help us with maintenance, performance, and diagnostics within our homes, buildings, and in the grid.”

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Portsmouth Wind Turbine Complaints highlight noise, shadow flicker, resident impacts, Town Council hearings, and Green Development mitigation plans near Portsmouth High School, covering renewable energy output, PPAs, and community compliance.

Key Points

Resident reports of noise and shadow flicker near Portsmouth High School, prompting review and mitigation efforts.

✅ Noise exceeds ambient levels seasonally, residents report fatigue.

✅ Shadow flicker lasts up to 90 minutes on affected homes.

✅ Town tasks developer to meet neighbors and propose mitigation.

The combination of the noise and shadows generated by the town’s wind turbine has rankled some neighbors who voiced their frustration to the Town Council during its meeting Monday.

Mark DePasquale, the founder and chairman of the company that owns the turbine, tried to reassure them with promises to address the bothersome conditions.

David Souza, a lifelong town resident who lives on Lowell Drive, showed videos of the repeated, flashing shadows cast on his home by the three blades spinning.

“I am a firefighter. I need to get my sleep,” he said. “And now it’s starting to affect my job. I’m tired.”

Town Council President Keith Hamilton tasked DePasquale with meeting with the neighbors and returning with an update in a month. “What I do need you to do, Mr. DePasquale, is to follow through with all these people.”

DePasquale said he was unaware of the flurry of complaints lodged by the residents Monday. His company had only heard of one complaint. “If I knew there was an issue before tonight, we would have responded,” he said.

His company, Green Development LLC, formerly Wind Energy Development LLC, installed the 279-foot-tall turbine near Portsmouth High School that started running in August 2016, as offshore developers like Deepwater Wind in Massachusetts plan major construction nearby. It replaced another turbine installed by a separate company that broke down in 2012.

In November 2014, the town signed an agreement with Wind Energy Development to take down the existing turbine, pay off the remaining $1.45 million of the bond the town took out to install it and put up a new turbine, amid broader legal debates like the Cornwall wind farm ruling that can affect project timelines.

In exchange, Wind Energy Development sells a portion of the energy generated by the turbine to the town at a rate of 15.5 cents per kilowatt hour for 25 years. Some of the energy generated is sold to the town of Coventry.

“We took down (the old turbine) and paid off the debt,” DePasquale said, noting that cancellations can carry high costs as seen in Ontario wind project penalties for scrapping projects. “I have no problem doing whatever the council wants … There was an economic decision made to pay off the bond and build something better.”

The turbine was on pace to produce 4 million-plus kilowatt hours per year, Michelle Carpenter, the chief operating officer of Wind Energy Development, said last April. It generates enough energy to power all municipal and school buildings in town, she said, while places like Summerside’s wind power show similarly strong output.

The constant stream of shadows cast on certain homes in the area can last for as long as an hour-and-a-half, according to Souza. “We shouldn’t have to put up with this,” he said.

Sprague Street resident John Vegas said the turbine’s noise, especially in late August, is louder than the neighborhood’s ambient noise.

“Throughout the summer, there’s almost no flicker, but this time of year it’s very prominent,” Vegas added. “It can be every day.”

He mentioned neighbors needed to be better organized to get results.

“When the residents purchased our properties we did not have this wind turbine in our backyard,” Souza said in a memo. “Due to the wind turbine … our quality of life has suffered.”

After the discussion, the council unanimously voted to allow Green Development to sublease excess energy to the Rhode Island Convention Center Authority, a similar agreement to the one the company struck with Coventry, as regional New England solar growth adds pressure on grid upgrade planning.

“This has to be a sustainable solution,” DePasquale said. “We will work together with the town on a solution.”

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COVID-19 Impact on Electricity Demand, per IEA data, shows 15% global load drop from lockdowns, with residential use up, industrial and service sectors down; fossil fuel generation fell as renewables and photovoltaics gained share.

Key Points

An overview of how lockdowns cut global power demand, boosted residential use, and increased the renewable share.

✅ IEA review shows at least 15% dip in daily global electricity load

✅ Lockdowns cut commercial and industrial demand; homes used more

✅ Fossil fuels fell as renewables and PV generation gained share

The daily demand for electricity dipped at least 15 per cent across the globe, according to Global Energy Review 2020: The impacts of the COVID-19 crisis on global energy demand and CO2 emissions, a report published by the International Energy Agency (IEA) in April 2020, even as global power demand surged above pre-pandemic levels.

The report collated data from 30 countries, including India and China, that showed partial and full lockdown measures adopted by them were responsible for this decrease.

Full lockdowns in countries — including France, Italy, India, Spain, the United Kingdom where daily demand fell about 10% and the midwest region of the United States (US) — reduced this demand for electricity.

Reduction in electricity demand after lockdown measures (weather corrected)

Source: Global Energy Review 2020: The impacts of the COVID-19 crisis on global energy demand and CO2 emissions, IEA

Drivers of the fall

There was, however, a spike in residential demand for electricity as a result of people staying and working from home. This increase in residential demand, though, was not enough to compensate for reduced demand from industrial and commercial operations.

The extent of reduction depended not only on the duration and stringency of the lockdown, but also on the nature of the economy of the countries — predominantly service- or industry-based — the IEA report said.

A higher decline in electricity demand was noted in countries where the service sector — including retail, hospitality, education, tourism — was dominant, compared to countries that had industrial economies.

The US, for example — where industry forms only 20 per cent of the economy — saw larger reductions in electricity demand, compared to China, where power demand dropped as the industry accounts for more than 60 per cent of the economy.

Italy — the worst-affected country from COVID-19 — saw a decline greater than 25 per cent when compared to figures from last year, even as power demand held firm in parts of Europe during later lockdowns.

The report said the shutting down of the hospitality and tourism sectors in the country — major components of the Italian economy — were said to have had a higher impact, than any other factor, for this fall.

Reduced fossil fuel dependency

Almost all of the reduction in demand was reportedly because of the shutting down of fossil fuel-based power generation, according to the report. Instead, the share of electricity supply from renewables in the entire portfolio of energy sources, increased during the pandemic, reflecting low-carbon electricity lessons observed during COVID-19.

This was due to a natural increase in wind and photovoltaic power generation compared to 2019 along with a drop in overall electricity demand that forced electricity producers from non-renewable sources to decrease their supplies, before surging electricity demand began to strain power systems worldwide.

The Power System Operation Corporation of India also reported that electricity production from coal — India’s primary source of electricity — fell by 32.2 per cent to 1.91 billion units (kilowatt-hours) per day, in line with India's electricity demand decline reported during the pandemic, compared to the 2019 levels.

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Covid-19 Energy Transition and Machine Learning reshape climate change policy, electricity planning, and grid operations, from demand forecasting and decarbonization strategies in Europe to scalable electrification modeling and renewable integration across Africa.

Key Points

How the pandemic reshapes energy policy and how ML improves planning, demand forecasts, and grid reliability in Africa.

✅ Pandemic-driven demand shifts strain grid operations and markets

✅ Policy momentum risks rollback; favor future-oriented decarbonization

✅ ML boosts demand prediction, electrification, and grid reliability in Africa

The impact of Covid-19 on the energy system was discussed in an online climate change workshop that also considered how machine learning can help electricity planning in Africa.

This year’s International Conference on Learning Representations event included a workshop held by the Climate Change AI group of academics and artificial intelligence industry representatives, which considered how machine learning can help tackle climate change and highlighted advances by European electricity prediction specialists working in this field.

Bjarne Steffen, senior researcher at the energy politics group at ETH Zürich, shared his insights at the workshop on how Covid-19 and the accompanying economic crisis are affecting recently introduced ‘green’ policies. “The crisis hit at a time when energy policies were experiencing increasing momentum towards climate action, especially in Europe, and in proposals to invest in smarter electricity infrastructure for long-term resilience,” said Steffen, who added the coronavirus pandemic has cast into doubt the implementation of such progressive policies.

The academic said there was a risk of overreacting to the public health crisis, as far as progress towards climate change goals was concerned.

Lobbying

“Many interest groups from carbon-intensive industries are pushing to remove the emissions trading system and other green policies,” said Steffen. “In cases where those policies are having a serious impact on carbon-emitting industries, governments should offer temporary waivers during this temporary crisis, instead of overhauling the regulatory structure.”

However, the ETH Zürich researcher said any temptation to impose environmental conditions to bail-outs for carbon-intensive industries should be resisted. “While it is tempting to push a green agenda in the relief packages, tying short-term environmental conditions to bail-outs is impractical, given the uncertainty in how long this crisis will last,” he said. “It is better to include provisions that will give more control over future decisions to decarbonize industries, such as the government taking equity shares in companies.”

Steffen shared with pv magazine readers an article published in Joule which can be accessed here, and which articulates his arguments about how Covid-19 could affect the energy transition.

Covid-19 in the U.K.

The electricity system in the U.K. is also being affected by Covid-19, even as the U.S. electric grid grapples with climate risks, according to Jack Kelly, founder of London-based, not-for-profit, greenhouse gas emission reduction research laboratory Open Climate Fix.

“The crisis has reduced overall electricity use in the U.K.,” said Kelly. “Residential use has increased but this has not offset reductions in commercial and industrial loads.”

Steve Wallace, a power system manager at British electricity system operator National Grid ESO recently told U.K. broadcaster the BBC electricity demand has fallen 15-20% across the U.K. The National Grid ESO blog has stated the fall-off makes managing grid functions such as voltage regulation more challenging.

Open Climate Fix’s Kelly noted even events such as a nationally-coordinated round of applause for key workers was followed by a dramatic surge in demand, stating: “On April 16, the National Grid saw a nearly 1 GW spike in electricity demand over 10 minutes after everyone finished clapping for healthcare workers and went about the rest of their evenings.”

Climate Change AI workshop panelists also discussed the impact machine learning could have on improving electricity planning in Africa. The Electricity Growth and Use in Developing Economies (e-Guide) initiative funded by fossil fuel philanthropic organization the Rockefeller Foundation aims to use data to improve the planning and operation of electricity systems in developing countries.

E-Guide members Nathan Williams, an assistant professor at the Rochester Institute of Technology (RIT) in New York state, and Simone Fobi, a PhD student at Columbia University in NYC, spoke about their work at the Climate Change AI workshop, which closed on Thursday. Williams emphasized the importance of demand prediction, saying: “Uncertainty around current and future electricity consumption leads to inefficient planning. The weak link for energy planning tools is the poor quality of demand data.”

Fobi said: “We are trying to use machine learning to make use of lower-quality data and still be able to make strong predictions.”

The market maturity of individual solar home systems and PV mini-grids in Africa mean more complex electrification plan modeling is required, similar to integrating AI data centers into Canada's grids at scale.

Modeling

“When we are doing [electricity] access planning, we are trying to figure out where the demand will be and how much demand will exist so we can propose the right technology,” added Fobi. “This makes demand estimation crucial to efficient planning.”

Unlike many traditional modeling approaches, machine learning is scalable and transferable. Rochester’s Williams has been using data from nations such as Kenya, which are more advanced in their electrification efforts, to train machine learning models to make predictions to guide electrification efforts in countries which are not as far down the track.

Williams also discussed work being undertaken by e-Guide members at the Colorado School of Mines, which uses nighttime satellite imagery and machine learning to assess the reliability of grid infrastructure in India, where new algorithms to prevent ransomware-induced blackouts are also advancing.

Rural power

Another e-Guide project, led by Jay Taneja at the University of Massachusetts, Amherst – and co-funded by the Energy and Economic Growth program on development spending based at Berkeley – uses satellite imagery to identify productive uses of electricity in rural areas by detecting pollution signals from diesel irrigation pumps.

Though good quality data is often not readily available for Africa, Williams added, it does exist.

“We have spent years developing trusting relationships with utilities,” said the RIT academic. “Once our partners realize the value proposition we can offer, they are enthusiastic about sharing their data … We can’t do machine learning without high-quality data and this requires that organizations can effectively collect, organize, store and work with data. Data can transform the electricity sector, as shown by Canadian projects to use AI for energy savings, but capacity building is crucial.”

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Greengen Misfeasance Ruling details a B.C. Supreme Court decision awarding $10.125 million over wrongfully denied Crown land and water licence permits for a Fries Creek run-of-river hydro project under a BC Hydro contract.

Key Points

A B.C. Supreme Court ruling awarding $10.125M for wrongful denial of Crown land and water licences on Greengen's project.

✅ $10.125M damages for misfeasance in public office

✅ Denial of Crown land tenure and water licence permits

✅ Tied to Fries Creek run-of-river and BC Hydro EPA

A B.C. Supreme Court judge has ordered the provincial government to pay $10.125 million after it denied permits to a company that wanted to build a run-of-the river independent power project near Squamish.

In his Oct. 10 decision, Justice Kevin Loo said the plaintiff, Greengen Holdings Ltd., “lost an opportunity to achieve a completed and profitable hydro-electric project” after government representatives wrongfully exercised their legal authority, a transgression described in the ruling as “misfeasance,” with separate concerns reflected in an Ontario market gaming investigation reported elsewhere.

Between 2003 and 2009, the company sought to develop a hydro-electric project on and around Fries Creek, which sits opposite the Brackendale neighbourhood on the other side of the Squamish River. To do so, Greengen Holdings Ltd. required a water licence from the Minister of the Environment and tenure over Crown land from the Minister of Agriculture.

After a lengthy process involving extensive communications between Greengen and various provincial and other ministries and regulatory agencies, the permits were denied, according to Loo. Both decisions cited impacts on Squamish Nation cultural sites that could not be mitigated.

Elsewhere, an Indigenous-owned project in James Bay proceeded despite repeated denials, underscoring varied approaches to community participation.

40-year electricity plan relied on Crown land
The case dates back to December 2005, when BC Hydro issued an open call for power with Greengen. The company submitted a tender several months later.

On July 26, 2006, BC Hydro awarded Greengen an energy purchase agreement, amid evolving LNG electricity demand across the province, under which Greengen would be entitled to supply electricity at a fixed price for 40 years.

Unlike conventional hydroelectric projects, such as new BC generating stations recently commissioned, which store large volumes of water in reservoirs, and in so doing flood large tracts of land, a run of the river project often requires little or no water storage. Instead, from a high elevation, they divert water from a stream or river channel.

Water is then sent into a pressured pipeline known as a penstock, and later passed through turbines to generate electricity, Loo explained, as utilities pursue long-term plans like the Hydro-Québec strategy to reduce fossil fuel reliance. The system returns water to the original stream or river, or into another body of water. 

The project called for most of that infrastructure to be built on Crown land, according to the ruling.

All sides seemed to support the project
In early 2005, company principle Terry Sonderhoff discussed the Fries Creek project in a preliminary meeting with Squamish Nation Chief Ian Campbell.

“Mr. Sonderhoff testified that Chief Campbell seemed supportive of the project at the time,” Loo said.

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Alberta wind power surpasses coal as AESO reports record renewable energy feeding the grid, with natural gas conversions, solar growth, energy storage, and decarbonization momentum lowering carbon intensity across Alberta's electricity system.

Key Points

AESO data shows wind surpassing coal in Alberta, driven by coal retirements, gas conversions, and growing renewables.

✅ AESO reports wind output above coal several times this week

✅ Coal units retire or convert to natural gas, boosting renewables

✅ Carbon intensity falls; storage and solar improve grid reliability

Marking a significant shift in Alberta energy history, wind generation trends provided more power to the province's energy grid than coal several times this week.

According to data from the Alberta Energy System Operator (AESO) released this week, wind generation units contributed more energy to the grid than coal at times for several days. On Friday afternoon, wind farms contributed more than 1,700 megawatts of power to the grid, compared to around 1,260 megawatts from coal stations.

"The grid is going through a period of transformative change when we look at the generation fleet, specifically as it relates to the coal assets in the province," Mike Deising, AESO spokesperson, told CTV News in an interview.

The shift in electricity generation comes as more coal plants come offline in Alberta, or transition to cleaner energy through natural gas generation, including the last of TransAlta's units at the Keephills Plant west of Edmonton.

Only three coal generation stations remain online in the province, at the Genesee plant southwest of Edmonton, as the coal phase-out timeline advances. Less available coal power, means renewable energy like wind and solar make up a greater portion of the grid.

EVOLUTION OF THE GRID
"Our grid is changing, and it's evolving," Deising said, adding that more units have converted to natural gas and companies are making significant investments into solar and wind energy.

For energy analyst Kevin Birn with IHS Markit, that trend is only going to continue.

"What we've seen for the last 24 to 36 months is a dramatic acceleration in ambition, policy, and projects globally around cleaner forms of energy or lower carbon forms of energy," Birn said.

Birn, who is also chief analyst of Canadian Oil Markets, added that not only has the public appetite for cleaner energy helped fuel the shift, but technological advancements have made renewables like wind and solar more cost-efficient.

"Alberta was traditionally heavily coal-reliant," he said. "(Now) western Canada has quite a diverse energy base."

LESS CARBON-INTENSIVE
According to Birn, the shift in energy production marks a significant reduction in carbon emissions as Alberta progresses toward its last coal plant closure milestone.

Ten years ago, IHS Markit estimates that Alberta's grid contributed about 900 kilograms of carbon dioxide equivalent per megawatt-hour of energy generation.

"That (figure is) really representing the dominance and role of coal in that grid," Birn said.

Current estimates show that figure is closer to 600 kilograms of CO2 equivalent.

"That means the power you and I are using is less carbon-intensive," Birn said, adding that figure will continue to fall over the next couple of years.

RENEWABLES HERE TO STAY
While many debate whether Alberta's energy is getting clean enough fast enough, Birn believes change is coming.

"It's been a half-decade of incredible price volatility in the oil market which had really dominated this sector and region," the analyst said.

"When I think of the future, I see the power sector building on large-scale renewables, which means decarbonization, and that provides an opportunity for those tech companies looking for clean energy places to land facilities."

Coal and natural gas are considered baseline assets by the AESO, where generation capacity does not shift dramatically, though some utilities report declining coal returns in other markets.

"Wind is a variable resource. It will generate when the wind is blowing, and it obviously won't when the wind is not," Deising said. "Wind and solar can ramp quickly, but they can drop off quite quickly, and we have to be prepared.

"We factor that into our daily planning and assessments," he added. "We follow those trends and know where the renewables are going to show up on the system, how many renewables are going to show up."

Deising says one wind plant in Alberta currently has an energy storage capacity to preserve renewably generated electricity during summer demand records and peak hours as needed. As the technology becomes more affordable, he expects more plants to follow suit.

"As a system operator, our job is to make sure as (the grid) is evolving we can continue to provide reliable power to Albertans at every moment every day," Deising said. "We just have to watch the system more carefully." 

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