FORECAST SOLAR IRRADIANCE USING ARTIFICIAL NEURAL NETWORKS VIA ASSESSMENT OF ROOT MEAN SQUARE ERROR

Abstract

Forecasting solar irradiance has been an important topic and a trend in
renewable energy supply share. Exact irradiance forecasting could help facilitate
the solar power output prediction. Forecasting improves the planning and
operation of the Photovoltaic (PV) system and the power system, then yields many
economic advantages. The irradiance can be forecasted using many methods with
their accuracies. This paper suggests two methods based on AI which approach
forecasting solar irradiance by getting data from solar energy resources and
Meteorological data on the Internet as inputs to an Artificial Neural Network (ANN)
model. Since the inputs involved are the same as the ones available from a recently
validated forecasting model, there are root mean square error (RMSE) and mean
absolute error (MAE) comparisons between the established forecasting models and
the proposed ones.