Short-term load forecasting of regional distribution network based on generalized regression neural network optimized by grey wolf optimization algorithm

Abstract

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems, which not only requires high accuracy and fast calculation speed, but also has a diversity of influential factors and strong randomness. This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient, factor analysis, gray wolf optimization, and generalized regression neural network (MIC-FA-GWO-GRNN). To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model, MIC is first used to quantify the non-linear correlation between the load and input features, and to eliminate the ineffective features, and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features. After that the high-precision short-term load forecasting based on GWO-GRNN model is realized. GRNN is used to regressively analyze the input features after screening and dimension reduction, and the parameter of GRNN is optimized by using the GWO, which has strong global searching ability and fast convergence. Finally a case study of a regional distribution network in Tianjin, China verifies the accuracy and applicability of the proposed forecasting model.