Combination model for day-ahead solar forecasting using local and global model input

Abstract

Numerical Weather Prediction (NWP) models over limited areas enable the simulation of local atmospheric processes in more detail and with a higher degree of accuracy when compared to global models. Limited-area NWP models can outperform their global counterparts due to higher resolution (ability to explicitly simulate processes) and tailored physics (global models, unless run as a physics ensemble, have one set of parameterization schemes for the whole globe). However, increased accuracy from an NWP model is not guaranteed and can vary based on the location and variable of interest. In this paper, we present a method for combining the output of a limited-area NWP model, the Weather Research and Forecasting model (WRF) and its global model—the European Center for Medium Range Weather Forecasting (ECMWF) deterministic model. We simulate day-ahead global horizontal irradiance for a location in Qinghai, China. WRF model configurations optimized by the type of day (cloud amount) are then implemented based on the ECMWF model forecast of cloud amount. A regression model to combined ECMWF and WRF model forecasts is then trained. The optimized coefficients (weights) of ECMWF and WRF show increasing WRF importance with higher cloud amounts and the combination out-performs the ECMWF input by 5.2% and the best WRF configuration by 7.2% on a 2.5-month testing set. The performance of the combined model increased with observed cloud amount where the combined model out-performed the ECMWF model by 12.6% for cloudy days indicating the relative importance of physical downscaling for the simulation of clouds.