Non-invasive load-shed authentication model for demand response applications assisted by event-based non-intrusive load monitoring

With today's growth of prosumers and renewable energy resources, it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation. In this context, demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies. However, effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders. This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications, assisted by an improved event-based non-intrusive load monitoring approach. For the said purposes, an improved event detection algorithm and machine learning model: support vector machine with a combination of genetic algorithm and GridSearchCV, is presented. This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario. In the given context, all the simulations are carried out on low sampling real-world load measurements: Pecan Street-Dataport, where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes. Based on the presented case study and analysis of the results, it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification. Moreover, it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.