Solar Energy Forecasting
a hybrid physical/ML model for sudden irradiance shifts
a hybrid physical/ML model for sudden irradiance shifts
an AI-native digital twin purpose-built for wind's volatility
predicting ice buildup on turbine blades up to 48 hours ahead with high accuracy
See how wind and solar operators use Renewcast's forecasts and trading signals to lower imbalance costs and capture more value from every MWh.
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nMAE (normalised Mean Absolute Error) is the average gap between forecast and actual production, expressed as a percentage of the plant's installed capacity: the lower, the better. In our real day-ahead cases, nMAE is 5.98% for wind and 1.67% for solar.
A model can win every benchmark and still cost you money. We work on what actually drives your financial result: local calibration, correction based on each site's historical patterns, and forecasting down to the individual turbine.
For wind, an AI-native digital twin of each plant, calibrated for high winds and rapid ramps. For solar, a physical model of the plant corrected by a machine learning layer.
A smaller gap between forecast and actual production means lower imbalance costs. On a 120 MW wind farm this can cut imbalance costs by 25-38%, worth around €1 million per year. We'll estimate the saving on your own portfolio together with you.
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