A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam, India

Abstract

This paper offers a novel method of power system load forecasting (PSLF) for regional special event days (RSEDs) when the load demand is highly prejudiced by societal considerations like cultural or religious rituals. These rituals abruptly change the consumer behaviors (demand variations) and it makes the load profile of such RSEDs more complex and nonlinear than normal holidays. Therefore, during RSEDs, an accurate PSLF method must integrate these consumer behaviors in the forecasting process. For this purpose, the offered method uses a new concept of similarity. The offered method is based on support vector machine (SVM) hybridized with a new algorithm called grey wolf optimizer (GWO) to access the proper parameter combinations of SVM for PSLF on RSEDs. This research is carried out in Assam and the novel method is designed to forecast electric power load demand in three RSEDs called Rongali Bihu, Durga Puja, and Diwali. The forecasting results of the offered method demonstrate superior accuracy while compared to the traditional method of training the PSLF system on the data of recent holidays. The efficacy of the offered method is upheld by comparing the forecasting performances with four different standard and recent methods namely, SVM, ANN, PSO-SVM and GA-SVM.