Evaluating Temperature-Dependent Consumers in a Demand Response Program using Machine Learning
One of the major goals in the European Union for reducing greenhouse gas emissions is the electrification of heat. Therefore, it is expected that the winter peak demand will rise significantly in the next few years. Demand Response could play an important role in reducing the need for network reinforcements by providing flexibility. The major motivation behind this paper is to evaluate the difference in demand flexibility between temperature-dependent consumers using electricity for heating and consumers using other energy sources. In this paper, temperature-dependent consumers are first identified by analyzing their smart metering data with machine learning. Further, the response of consumers is evaluated using probabilistic baseline models. The results show that heat electrification will increase the demand during low temperatures, whereas these consumers will also be able to offer far more flexibility during low temperatures and high demand. To the best of our knowledge, there is no empirical study, that would investigate these using state of the art methods in such detail. The paper presents part of the analyses that were carried out after the real demand response program in the scope of the Slovenian-Japanese NEDO project.