Amperon

Traditional renewable forecasts assume metered generation is ground truth. Frequency regulation breaks that assumption. See why IPPs need forecasts that separate operator intervention from true solar and wind potential.

This is Amperon's grid short-term forecast: demand curves running across European grids including France, Germany, Great Britain, Italy, Spain, and more, updated every hour, benchmarked live against TSO estimates. Sub-hourly granularity is available down to every 15 minutes for European intra-day markets. One platform covers grid forecasting for traders and market participants, and meter and portfolio forecasting for suppliers, BRPs, and aggregators. All running on the same model: 40,000+ weather points, retrained hourly, probabilistic outputs. If you want to see how Amperon performs against your current forecast, we can run a backtest on your markets. Grid forecasts deploy in days.

Amperon forecasts 260+ solar and wind assets, site-specific models built on each asset's historical generation, equipment configuration, and hyper-local weather data from 40,000+ observation points. Solar targets 4–6% cnMAE. Wind targets 7–15% cnMAE. Both products deliver sub-hourly resolution down to every 5 minutes, covering day-ahead through 14 days. Probabilistic outputs on every forecast, confidence intervals you can trade and dispatch against, not just a point estimate. Models retrain every hour. Average deployment: approximately 3 weeks from contract to live forecast. SOC 2 Type II certified. European asset operators use Amperon to reduce imbalance penalties, sharpen day-ahead bids, and improve intra-day dispatch decisions. The accuracy difference between a regional model and a site-specific, hourly-retrained model shows up in every settlement period. See what Amperon's renewable asset forecasting looks like for your portfolio.

A solar farm in southern Spain and a wind farm on the North Sea don't behave the same way. The weather patterns are different. The irradiance and wind regimes are different. The grid interconnection dynamics are different. A regional model averages across all of that, and the averaging is where the accuracy goes. Amperon's renewable asset forecasting is built at the asset level. Site-specific models trained on each asset's historical generation, equipment configuration, and hyper-local weather from 40,000+ observation points. Solar and wind. Resolution down to every 5 minutes. Models retrain every hour. Probabilistic outputs so you can plan for the full distribution of outcomes, not just the median. 260+ solar and wind assets currently forecasted. Average deployment: approximately 3 weeks. European asset operators use Amperon to sharpen bids, reduce imbalance penalties, and improve dispatch decisions at the asset level. If your renewable portfolio is running on a regional model with a slow update cycle, you're leaving accuracy on the table in every operating hour.

Amperon's load forecasting suite is 3x more accurate than TSO day-ahead forecasts , validated across 15+ European grids including France, Germany, Great Britain, Italy, Spain, the Netherlands, and Belgium. The suite covers grid demand forecasting for traders and market participants, sub-hourly forecasting for intra-day position management, and meter and portfolio demand forecasting for suppliers, BRPs, and aggregators. Sub-hourly granularity is available down to every 15 minutes, matching the time increment of European intra-day markets. All products retrain every hour on 40,000+ hyper-local weather observation points. 140+ customers worldwide. $1B+ cumulative P&L impact. Average customer ROI of 10x. SOC 2 Type II certified. Grid forecasts deploy in days. If your current load forecast is starting from a TSO estimate or running on a 6-hour update cycle, there is a more accurate option available for European markets today.

Amperon Built a Free Excel Template So You Can Calculate Your Wind and Solar Forecast Accuracy Right Now. Download the template, run your own data, and see where you stand. Most renewable asset operators know their forecast isn't perfect. Very few have actually quantified how imperfect it is. cnMAE, capacity-normalized mean absolute error, is the industry standard metric for measuring wind and solar forecast accuracy. It accounts for the size of your asset so you can compare performance across different generators, different ISOs, and different forecast providers on equal footing. Amperon built a free Excel template that calculates it for you. Paste in your forecasts and actual generation from the last year, add your asset metadata, and the template returns your overall accuracy statistics and your peak-hour accuracy, the number that matters most for Day-Ahead bidding and imbalance exposure. No formulas to build. No data science required. If you have your generation data in a spreadsheet, you can have your cnMAE in minutes. It's also the first honest look at whether your current forecast provider is actually performing. The numbers either hold up or they don't.