Categorization of Indian residential consumers electrical energy consumption pattern using clustering and classification techniques

Abstract

Data Mining techniques are widely applied in power sector to analyze the electricity consumption pattern(ECP) of consumers for implementing effective demand response management. In this study, the ECP of LV consumers of Karur city is used for evaluation. The dataset consists of five years ECP. In this study, K-means clustering algorithm is used to categorize the data into binary and three classes. The results of the K-means clustering technique is used for developing eight classifier models. The hyperparameters of the classifier models are tuned using grid search optimization technique. The classification process applies hold-out partitioning and stratified 10-fold cross-validation(CV) techniques to evaluate the classifier’s efficiency in predicting a consumer’s ECP. The experimental result shows that the K-means clustering has minimal overlapping of 0.48% and 1.98% for binary and three-class clustering processes. Hence, the K-means algorithm results are used to build this study’s classification model. In the classification process, the experimental results of LR and SVM algorithms have better accuracy in binary and three class categories respectively, for the hold-out partitioning method. Meanwhile, the SVM also has better generalization ability and is validated using stratified 10-fold CV, achieving an accuracy of 99.09% and 98.19% for binary and three classes respectively, in the classification of consumer data.