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 aggre- gated 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 Inte- grated 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.
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