As the demand for more sustainable energy solutions grows, the intermittent nature of solar and wind generation poses challenges for asset owners who want to effectively bid into the day ahead and real-time markets. The energy transition will increasingly depend on improving the reliability of wind and solar to maintain an efficient and stable grid, especially as power plants are retired and demand continues to grow.
Between 2024 and 2030, all forms of renewable generation are expected to double, which means independent power producers (IPPs), utilities, and energy traders are staying ahead of the curve by using advanced forecasting methodologies to provide a more granular and, more importantly, accurate view of the grid and their assets.
Without accurate forecasts, renewable generation forecasts can result in substantial financial losses. Independent power producers (IPPs) and energy traders who use short-term solar forecasts need to determine how much power to sell into day-ahead markets, and whether to reserve any for real-time markets. They also use renewable generation forecasts to help determine whether the market is currently over- or underpriced when making decisions about mid- and long-term hedging.
Utilities, too, are increasingly dependent on accurate renewable forecasts to predict what their net demand will be. With their own renewable generation capacity and responsibility for meeting customer demand, utilities need precise forecasts to estimate net demand. Without this visibility, they risk being forced to buy additional power in high-cost, real-time markets.
But getting these forecasts right can be a challenge. Solar and wind power output fluctuates due to weather conditions, cloud cover, seasonal variations, and human-based maintenance factors.
At first glance, solar seems simple to forecast as the sun rises and sets predictably each day. But our data scientists would disagree with this sentiment. A key variable is the "asset state," which includes the condition of the solar panels — whether they’re clean, dirty, shaded, or undergoing maintenance. These factors can shift unpredictably and are difficult to model, introducing a layer of uncertainty. Despite these challenges, Amperon maintains a best-in-class solar forecasting benchmark with a cnMAE (capacity-normalized Mean Absolute Error) of 4–6%.
Compared to solar, wind can be trickier to forecast due to its inherent high variability across shorter distances. Local terrain — such as mountains, valleys, and large bodies of water — can significantly alter wind patterns. As a result, wind forecasting tends to be less accurate than solar, with industry benchmarks around 11% cnMAE. Amperon, however, sets itself apart with a wind forecasting benchmark of 8–12%, outperforming many competitors.
Any forecast is going to have a margin of predictive error, but in volatile markets, even small inaccuracies can lead to big financial consequences. “If you’re using solar or wind forecasts, it’s important to know just how accurate they are, because errors can be magnified in your trading strategies,” explained Amperon’s Vice President of Markets and Technical Services, Elliot Chorn. “For instance, if you decide to sell 100 percent of your solar generation, but your forecast is significantly off, you may only sell 90 percent of it. Or worse, you may sell 110 percent.”
Amperon’s Approach to Asset Wind & Solar Forecasting
While both types of generation share common forecasting hurdles, Amperon’s approach addresses these challenges with tailored solutions for each, leveraging machine learning (ML), with innovative modeling methods, and a physics-based approach.
1. Ensemble Weather Forecasting for Wind and Solar
Variables such as wind speed, wind gusts, irradiance, cloud cover, and temperature directly influence generation output. Historically, relying on a single weather vendor introduces errors that impact forecast accuracy. To address this, Amperon has developed a dynamically weighted ensemble weather model for both wind and solar generation. This model intelligently integrates multiple weather vendors and adjusts their weighting on an hourly basis, taking into account the accuracy of each vendor’s forecast. By combining the strengths of different sources, we minimize the risk of errors from relying on a single provider.
2. Integrating Historical Asset Availability and Curtailment Data
Accurate generation forecasting also requires context around past performance, especially to identify causes of underperformance. Both for wind and solar assets, factors like utility-imposed curtailment or scheduled maintenance downtime can cause reduced generation, which is crucial to distinguish from weather-related factors (e.g., low wind speeds or cloud cover). Amperon enables customers to upload historical curtailment, potential power, and plant availability data. By integrating this additional information, Amperon’s ML models can differentiate between performance impacts due to weather and those caused by exogenous factors. This integration helps ensure more precise predictions and is available in both our solar and wind short-term and sub-hourly forecast products.
3. Incorporating Physical Asset Attributes
Solar and wind forecasting traditionally rely heavily on physics-based models, which estimate generation output based on physical characteristics (e.g., panel orientation, efficiency, turbine height). While useful, these models have inherent limitations, especially when dealing with limited historical data. Amperon has enhanced our renewable forecasts by incorporating physical asset attributes into our ML models. This hybrid approach combines the benefits of physics-based modeling with the accuracy of ML-driven predictions. It improves forecast precision, particularly for assets with limited historical generation data. This feature is available in both our solar and wind short-term and sub-hourly forecast.
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Amperon’s wind and solar generation forecasts deliver hourly updates up to 15 days and unparalleled granularity of up to 5-minute intervals, empowering utilities and IPPs to maximize financial efficiency and operational reliability.
With unmatched accuracy and granularity, Amperon customers avoid these common issues:
One of the most important advancements in renewable generation forecasting is the emerging prevalence of a new accuracy metric: cnMAE.
Traditional forecasting metrics like MAPE, nMAE, and RMSE, fall short when dealing with renewable generation.
Dive deeper with our cnMAE Whitepaper.
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