Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression

Abstract

Renewable power output is an important factor in scheduling and for improving balanced area control performance. This investigation develops an evolutionary seasonal decomposition least-square support vector regression (ESDLS-SVR) to forecast monthly solar power output. The construction of the ESDLS-SVR uses seasonal decomposition and least-square support vector regression (LS-SVR). Genetic algorithms (GA) are used simultaneously to select the parameters of the LS-SVR. Monthly solar power output data from Taiwan Power Company are used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance in terms of forecasting accuracy. A comparative study has been introduced showing that the ESDLS-SVR model performance is better than autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), generalized regression neural network (GRNN) and LS-SVR models.