Identifying susceptible consumers for demand response and energy efficiency policies by time-series analysis and supplementary approaches

Demand side management(DSM) is one of the most important concepts in present restructured market, which tries to maximize benefits for both suppliers and consumers. DSM could be implemented in two ways: model-driven and data-driven methods. in the first case, a model is provided by using dataset related to previous records and in the second case, analysis is done by real-time data. In this way, energy efficiency(EE) and demand response(DR) are two famous policies whose performance could show satisfactory and persuasive results, so, for this case are of interest. Appropriate implementation of these programs necessitates sufficient knowledge about consumers' behavior in their electricity energy usage. In recent years, with advent of Advanced Metering Infrastructure (AMI), which created proper communication infrastructure, the access to real-time data of the grid is provided. So, the required data for data-driven DSM is available now. By this hypothesis, the goal of this paper is providing more complete and accurate information about consumers' behavior in consumption, to identify the susceptible consumers for DR and EE policies. This goal is achieved by simultaneously applying the data-mining algorithms for time-series analysis and the entropy index. The susceptibility of customers for proposing tariffs is analyzed too. For evaluation of the procedure, a real dataset, provided from the Irish Social Science Data Archive(ISSDA) is used.