A data analytical approach using support vector machine for demand response management in smart grid

Abstract

Many existing issues pertaining to power sector such as-demand response management, theft detection, outage management etc. can be solved efficiently with grid modernization. Out of these, demand response is one such issue which affects the overall grid stability. One way of managing demand response is to balance the load in smart grid (SG). In this paper, a novel scheme for handling the demand response in SG is presented. The household load is managed in such a way that the load profile of the SG is flattened. Unlike existing approaches, the proposed scheme is based on the data analytics and works as follows. Initially, the data is gathered from all the devices and the users with excess load consumption are identified using the support vector machine (SVM). To curtail the load of such users, a novel load balancing algorithm has been designed. This algorithm sheds the excess load in homes so as to balance the overall load in SG. The simulation results show that the proposed scheme effectively flattens the load profile of SG while managing the demand response.