Solar radiation forecast with machine learning

Renewable energy forecasting becomes increasingly important as the contribution of solar/wind power production to the electrical power grid constantly increases. Significant improvement in forecasting accuracy has been demonstrated by developing more sophisticated solar irradiance forecasting models using statistics and/or numerical weather predictions. In this presentation, we report the development of a machine-learning based multi-model blending approach for statistically combing multiple meteorological models to improve the accuracy of solar power forecasting. The system leverages upon multiple existing physical models for prediction including numerous atmospheric and cloud prediction models based on satellite imagery as well as numerical weather prediction (NWP) products.

1. Introduction

Accurate forecasts of the atmospheric state have been a particularly challenging problem yet have enormous social and economic benefit.¹⁾ Among many important applications of atmospheric forecast is the forecast of variable solar/wind energy generation, which is becoming crucial with increasing penetration of wind/solar energy to the total energy mix. A more accurate forecast enables reliable grid operation at reduced COSt.^2–6)