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

• An ESDLS-SVR model was developed to forecast monthly solar power output data.
• The ESDLS-SVR model can obtain satisfying forecasting results.
• The developed mode can forecast solar power output effectively.

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.