Solar Power Forecasting Using Artificial Neural Networks

In recent years, the rapid boost of variable energy generations particularly from wind and solar energy resources in the power grid has led to these generations becoming a noteworthy source of uncertainty with load behavior still being the main source of variability. Generation and load balance is required in the economic scheduling of the generating units and in electricity market trades. Energy forecasting can be used to mitigate some of the challenges that arise from the uncertainty in the resource. Solar power forecasting is witnessing a growing attention from the research community. The paper presents an artificial neural network model to produce solar power forecasts. Sensitivity analysis of several input variables for best selection, and comparison of the model performance with multiple linear regression and persistence models are also shown.