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. The ERCOT region, specifically, has massive wind and solar capacity. Net-demand forecasts (total demand minus renewable generation) help predict when conventional generation will be needed to fill gaps from variable renewable resources. Market participants also use forecasts as a risk management tool. Accurate projections allow participants to hedge their positions through bilateral contracts or financial instruments, protecting against volatile market conditions. Meanwhile, forecasts can provide insight for operational planning. Having market predictions for up to 15 days can help managers with unit commitment decisions, maintenance scheduling, and resource allocation across a portfolio of generation assets. In Texas, the competitive energy-only market design places even greater importance on forecasting, as there are no capacity payments—generators earn revenue solely when they produce energy. The state’s isolated grid, extreme weather events, and high renewable penetration make accurate forecasting both more challenging and more financially consequential than in many other markets. 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.”