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

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Energy and AI 2021年第1期3卷 180-191页

作者： Attique Ur Rehman Tek Tjing Lie brice valls Shafiqur Rahman Tito School of Engineering Computerand Mathematical SciencesAuckland University of TechnologyNew Zealand brice Valles Consulting New Zealand School of Professional Engineering Manukau Institute of TechnologyNew Zealand

With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid *** 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 ***,effective demand response deployment faces numerous challenges including trust deficit among the concerned *** 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 *** the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is *** paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life *** 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 *** 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 ***,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.

关键词： Non-Intrusive Load Monitoring Load-Shed Authentication Demand Response Machine Learning Model Genetic Algorithm Energy Efficiency

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