Forecasting Solar Photovoltaic power production at the aggregated system level

Abstract

Solar Photovoltaic power production has grown significantly over the past few years. California ISO is the first system operator in North America to make the data for aggregated system-level solar power production across its territory available on a regular basis. In this paper, we demonstrate the application of three well-established forecasting models to 24-hour-ahead prediction of solar power at the system level. The models investigated in this paper include Auto Regressive Integrated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Least Squares Support Vector Machine (LS-SVM). Numerical results and discussions are provided based on California ISO solar power data.