In the high-stakes world of energy trading, accurate net-demand energy forecasts are not just useful; they are indispensable. For market participants in regions like the Electric Reliability Council of Texas (ERCOT), these forecasts are the lifeblood of competitive strategy. They predict price spikes or crashes, optimize bidding strategies, and guide asset management. In ERCOT, with its vast wind and solar capacity, net-demand forecasts are crucial for balancing variable renewable resources with conventional generation. They also serve as a risk management tool, allowing participants to hedge positions and protect against market volatility. For operational planning, forecasts up to 15 days ahead help managers with unit commitment, maintenance scheduling, and resource allocation. In Texas’s energy-only market, where generators earn revenue solely from energy production, accurate forecasting is even more critical. The state’s isolated grid, extreme weather, and high renewable penetration make forecasting both challenging and financially consequential.
Enter artificial intelligence (AI), a game-changer in energy forecasting. AI algorithms can process vast amounts of meter and weather data, producing highly accurate forecasts that outperform human analysts. Sean Kelly, co-founder and CEO of Amperon, an AI-powered forecasting solutions provider, highlights the evolution from simple Excel spreadsheets to complex machine-learning models. “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. This complexity underscores the need for advanced forecasting systems, not just for utilities but also for independent power producers and energy traders.
The stakes are high, as illustrated by the devastating Winter Storm Uri in February 2021. Wholesale prices in ERCOT skyrocketed from around $50/MWh to the cap of $9,000/MWh, lasting for approximately 4.5 days. This 180-fold increase led to severe financial impacts, with several retail electricity providers, including Griddy Energy and Brazos Electric Power Cooperative, facing bankruptcy. Amperon’s early and accurate forecasts helped some clients mitigate these impacts by buying power at lower rates before the price surge. “Our forecasts go out 15 days, ERCOT’s forecasts only go out seven,” Kelly explained. “So, we told everyone, ‘Alert! Alert! This is coming!’”
Following Winter Storm Uri, regulatory reforms were implemented, but accurate forecasting remains vital. With more renewables entering the grid, the market is becoming increasingly binary—either extremely low or extremely high prices. This trend makes accurate forecasting even more crucial. Kelly emphasizes the need for continuous improvement and adoption of new technologies to stabilize the grid. “This job is getting harder and harder by the day—both for the software companies, but really for those load serving entities,” Kelly said. “So, that’s where we’ve got to adopt new technologies and always continue to better ourselves, better our knowledge of the new things coming down the pipe, and just work together to make the grid a much more stable place.”
The implications of this news for the energy sector are profound. As renewable energy penetration increases, the need for precise forecasting will only grow. Market participants must invest in advanced AI-driven forecasting tools to navigate the complexities of an ever-changing energy landscape. The lessons from Winter Storm Uri serve as a stark reminder of the financial and operational risks at stake. The future of energy trading will likely see a greater reliance on AI and machine learning, driving innovation and enhancing market stability. The energy sector must embrace these technological advancements to ensure resilience and profitability in an increasingly volatile market.
