An improved short term load forecasting with ranker based feature selection technique

The load forecasting is the significant task carried out by the electricity providing utility companies for estimating the future electricity load. The proper planning, scheduling, functioning, and maintenance of the power system rely on the accurate forecasting of the electricity load. In this paper, the clustering-based filter feature selection is proposed for assisting the forecasting models in improving the short term load forecasting performance. The Recurrent Neural Network based Long Short Term Memory (LSTM) is developed for forecasting the short term load and compared against Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR) and Random Forest (RF). The performance of the forecasting model is improved by reducing the curse of dimensionality using filter feature selection such as Fast Correlation Based Filter (FCBF), Mutual Information (MI), and RReliefF. The clustering is utilized to group the similar load patterns and eliminate the outliers. The feature selection identifies the relevant features related to the load by taking samples from each cluster. To show the generality, the proposed model is experimented by using two different datasets from European countries. The result shows that the forecasting models with selected features produce better performance especially the LSTM with RReliefF outperformed other models.