Joint Feature Selection of Power Load in Time Domain and Frequency Domain Based on Whale Optimization Algorithm

Abstract

Accurate classification of power load is the premise of demand-side response capability evaluation, which is of great significance for demand-side management. Therefore, this study proposes a selection method of joint characteristics of power load in time domain and frequency domain based on whale optimization algorithm. The electrical measurement data of six typical electrical equipment are selected to form the original power load identification dataset. Firstly, the time domain features and frequency domain features of the power load data are extracted, and then the joint characteristics of the power load are screened by Whale optimization algorithm (WOA). Finally, the selected feature information is used as the input to verify the performance of WOA on the joint characteristics of power load under back propagation neural network (BP), extreme learning machine (ELM), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and naive Bayesian (NB) classifiers. The results show that WOA can effectively screen out the 15 most helpful feature attributes for power load identification, which can not only improve the accuracy of power load identification but also effectively reduce the time cost of the algorithm, which is of reference value for further demand-side response strategy. At the same time, it is of great significance for intelligent dispatching of power system and improving the economic operation of the power system.