Short Range Direct and Diffuse Irradiance Forecasts for Solar Energy Applications Based on Aerosol Chemical Transport and Numerical Weather Modeling

Abstract

This study deals with solar irradiance forecasts of the next two to three days with respect to their application in solar energy industries, such as yield prediction for the integration of the strongly fluctuating solar energy into the electricity grid. During cloud free situations, which are predominant in regions and time periods focused on by the solar energy industry, aerosols are the main atmospheric parameter which determines ground level direct and global irradiance. Therefore, for an episode of five months in Europe the accuracy of forecasts of the aerosol optical depth at 550 nm (AOD550) based on particle forecasts of a chemistry transport model (EURAD-CTM) is analyzed in a first step. It is shown that these aerosol forecast underestimate ground-based AOD550 measurements by a mean of -0.11 (RMSE 0.20). Using these aerosol forecasts together with other remote sensing data (ground albedo, ozone) and numerical weather prediction parameters (water vapor, clouds) a prototype for an irradiance forecasting system (AFSOL) is set up. Based on the five-month aerosol dataset its results are then compared to forecasts of the ECMWF and the MM5 model, to Meteosat-7 satellite data and to ground measurements. It is demonstrated that for clear sky situations the AFSOL system significantly improves global irradiance and especially direct irradiance forecasts compared to ECMWF forecasts (bias reduction from -26% to +11%, RMSE reduction from 31% to 19% for direct irradiance). On the other hand, the study shows that for cloudy conditions the AFSOL forecasts can lead to significantly larger forecast errors. This also justifies an increased research effort on cloud parameterization schemes, which is known as ongoing research. One practical solution for solar energy power plant operators in the meanwhile is to combine the different irradiance models depending on the forecasted cloud cover, which leads to significant reductions in bias for the overall period.