Day-ahead load forecasting based on Spearman-MultiTaskLasso-MLP

Abstract

Short term load forecasting is an important guarantee to ensure the smooth operation of power system, but there are many factors affecting it, which makes it difficult to predict it whit high-precision in the future. In order to improve the accuracy of load forecasting, a day ahead load forecasting model based on Spearman correlation coefficient, MultiTaskLasso and Multilayer Perceptron (MLP) is proposed in this paper. Firstly, Spearman correlation coefficient is used to select the leading steps in order to eliminate the low influencing factors related to load; Secondly, MultiTaskLasso is applied for feature extraction to extract the optimal feature set effectively that can best express the load fluctuation from different influencing factors with different candidate input variables; Finally, MLP is applied to predict the load with 48 lagging steps in the next 24 hours. Model comparison and verification results show that, the predict accuracy has increased by at least 24.38% and 25.32% respectively when the selection of the leading steps and the extraction of the optimal feature set have completed, and the prediction accuracy of Spearman-MultiTaskLasso-MLP is improved by at least 39.42%. The effect of the leading steps selection and feature extraction is obvious, and the prediction accuracy of each comparative model is improved significantly. The results of this study can provide a reference for the selection of leading steps and feature extraction for similar studies.