Mar 11 2025 29 mins 11
Net-demand energy forecasts are critical for competitive market participants, such as in the Electric Reliability Council of Texas (ERCOT) and similar markets, for several key reasons. For example, accurate forecasting helps predict when supply-demand imbalances will create price spikes or crashes, allowing traders and generators to optimize their bidding strategies. It’s also important for asset optimization. Power generators need to know when to commit resources to the market and at what price levels. Poor forecasting can lead to missed profit opportunities or operating assets when prices don’t cover costs.
Fortunately, artificial intelligence (AI) is now capable of producing highly accurate forecasts from the growing amount of meter and weather data that is available. The complex and robust calculations performed by these machine-learning algorithms is well beyond what human analysts are capable of, making advance forecasting systems essential to utilities. Plus, they are increasingly valuable to independent power producers (IPPs) and other energy traders making decisions about their positions in the wholesale markets.
Sean Kelly, co-founder and CEO of Amperon, a company that provides AI-powered forecasting solutions, said using an Excel spreadsheet as a forecasting tool was fine back in 2005 when he got started in the business as a power trader, but that type of system no longer works adequately today. “Now, we’re literally running at Amperon four to six models behind the scenes, with five different weather vendors that are running an ensemble each time,” Kelly said as a guest on The POWER Podcast. “So, as it gets more confusing, we’ve got to stay on top of that, and that’s where machine learning really kicks in.”
The consequences of being ill-prepared can be dire. Having early and accurate forecasts can mean the difference between a business surviving or failing. Effects from Winter Storm Uri offer a case in point.
Normally, ERCOT wholesale prices fluctuate from about $20/MWh to $50/MWh. During Winter Storm Uri (Feb. 13–17, 2021), ERCOT set the wholesale electricity price at its cap of $9,000/MWh due to extreme demand and widespread generation failures caused by the storm. This price remained in effect for approximately 4.5 days (108 hours). This 180-fold price increase had devastating financial impacts across the Texas electricity market.
The financial fallout was severe. Several retail electricity providers went bankrupt, most notably Griddy Energy, which passed the wholesale prices directly to customers, resulting in some receiving bills of more than $10,000 for just a few days of power.
“Our clients were very appreciative of the work we had at Amperon,” Kelly recalled. “We probably had a dozen or so clients at that time, and we told them on February 2 that this was coming,” he said.
With that early warning, Kelly said Amperon’s clients were able to get out in front of the price swing and buy power at much lower rates. “Our forecasts go out 15 days, ERCOT’s forecasts only go out seven,” Kelly explained. “So, we told everyone, ‘Alert! Alert! This is coming!’ Dr. Mark Shipham, our in-house meteorologist, was screaming it from the rooftops. So, we had a lot of clients who bought $60 power per megawatt. So, think about buying 60s, and then your opportunity is 9,000. So, a lot of traders made money,” he said.
“All LSEs—load serving entities—still got hit extremely bad, but they got hit a lot less bad,” Kelly continued. “I remember one client saying: ‘I bought power at 60, then I bought it at 90, then I bought it at 130, then I bought it at 250, because you kept telling me that load was going up and that this was getting bad.’ And they’re like, ‘That is the best expensive power I’ve ever bought. I was able to keep my company as a retail energy provider.’ And, so, those are just some of the ways that these forecasts are extremely helpful.”
Fortunately, artificial intelligence (AI) is now capable of producing highly accurate forecasts from the growing amount of meter and weather data that is available. The complex and robust calculations performed by these machine-learning algorithms is well beyond what human analysts are capable of, making advance forecasting systems essential to utilities. Plus, they are increasingly valuable to independent power producers (IPPs) and other energy traders making decisions about their positions in the wholesale markets.
Sean Kelly, co-founder and CEO of Amperon, a company that provides AI-powered forecasting solutions, said using an Excel spreadsheet as a forecasting tool was fine back in 2005 when he got started in the business as a power trader, but that type of system no longer works adequately today. “Now, we’re literally running at Amperon four to six models behind the scenes, with five different weather vendors that are running an ensemble each time,” Kelly said as a guest on The POWER Podcast. “So, as it gets more confusing, we’ve got to stay on top of that, and that’s where machine learning really kicks in.”
The consequences of being ill-prepared can be dire. Having early and accurate forecasts can mean the difference between a business surviving or failing. Effects from Winter Storm Uri offer a case in point.
Normally, ERCOT wholesale prices fluctuate from about $20/MWh to $50/MWh. During Winter Storm Uri (Feb. 13–17, 2021), ERCOT set the wholesale electricity price at its cap of $9,000/MWh due to extreme demand and widespread generation failures caused by the storm. This price remained in effect for approximately 4.5 days (108 hours). This 180-fold price increase had devastating financial impacts across the Texas electricity market.
The financial fallout was severe. Several retail electricity providers went bankrupt, most notably Griddy Energy, which passed the wholesale prices directly to customers, resulting in some receiving bills of more than $10,000 for just a few days of power.
“Our clients were very appreciative of the work we had at Amperon,” Kelly recalled. “We probably had a dozen or so clients at that time, and we told them on February 2 that this was coming,” he said.
With that early warning, Kelly said Amperon’s clients were able to get out in front of the price swing and buy power at much lower rates. “Our forecasts go out 15 days, ERCOT’s forecasts only go out seven,” Kelly explained. “So, we told everyone, ‘Alert! Alert! This is coming!’ Dr. Mark Shipham, our in-house meteorologist, was screaming it from the rooftops. So, we had a lot of clients who bought $60 power per megawatt. So, think about buying 60s, and then your opportunity is 9,000. So, a lot of traders made money,” he said.
“All LSEs—load serving entities—still got hit extremely bad, but they got hit a lot less bad,” Kelly continued. “I remember one client saying: ‘I bought power at 60, then I bought it at 90, then I bought it at 130, then I bought it at 250, because you kept telling me that load was going up and that this was getting bad.’ And they’re like, ‘That is the best expensive power I’ve ever bought. I was able to keep my company as a retail energy provider.’ And, so, those are just some of the ways that these forecasts are extremely helpful.”
