A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches.