Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria

A feature selection method based on the generalized minimum redundancy and maximum relevance (G-mRMR) is proposed to improve the accuracy of short-term load forecasting (STLF). First, mutual information is calculated to analyze the relations between the original features and the load sequence, as well as the redundancy among the original features. Second, a weighting factor selected by statistical experiments is used to balance the relevance and redundancy of features when using the G-mRMR. Third, each feature is ranked in a descending order according to its relevance and redundancy as computed by G-mRMR. A sequential forward selection method is utilized for choosing the optimal subset. Finally, a STLF predictor is constructed based on random forest with the obtained optimal subset. The effectiveness and improvement of the proposed method was tested with actual load data.