Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines

摘要

Short-Term Electricity Load Forecasting (STLF) has become one of the hot topics of energy research as it plays a crucial role in electricity markets and power systems. Few researches aim at selecting optimal input features (Feature Selection, FS) when forecasting model is established, although more and more intelligent hybrid models are developed to forecast real-time electricity load. In fact, a good FS is a key factor that influence prediction accuracy. Based on the idea of selecting optimal input features, a hybrid model, AS-GCLSSVM, is developed to forecast electricity load in this research, which combines ACF (AutoCorrelation Function) and LSSVM (Least Squares Support Vector Machines). ACF is applied to select the informative input variables, and LSSVM is for prediction. The parameters in LSSVM are optimized by GWO (Grey Wolf Optimization Algorithm) and CV (Cross Validation). The proposed model is to forecast the half-hour electricity load of the following week. Experimental results show that it is an effective approach that can improve the forecasting accuracy remarkably, compared with the benchmark models.