Machine Learning Based Multi-model Blending for Enhancing Renewable Energy Forecasting
More importantly , we demonstrate that in addition to parameters (solar/wind power, solar irradiances) to be predicted, including additional atmospheric state parameters which collectively define “weather situation categories” as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance is applied to show that the error of individual model typically has substantial dependence on “weather situation categories”. The machine-learning system effectively reduces such situation dependence error thus produces more accurate result compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Results using the system show over 30% improvement in solar irradiance/power forecast accuracy compared to forecast based on the best individual model.
The work is partially supported by Department of Energy SunShot Initiative contract #DE-EE0006017.