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Forecast solar irradiance using artificial neural networks via assessment of root mean square error

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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. |