Non-Intrusive Appliance Load Monitoring using Genetic Algorithms
[摘要] Smart Meters provide detailed energy consumption data and rich contextual information which can be utilized to assist energy providers and consumers in understanding and managing energy use. Here, we present a novel approach using genetic algorithms to infer appliance level data from aggregate load curves without a-priori information. We introduce a theoretical framework to encode load data in a chromosomal representation, to reconstruct individual appliance loads and propose several fitness functions for the evaluation. Our results, using artificial and real world data, confirm the practical relevance and feasibility of our approach.
[发布日期] [发布机构] Frankfurt University of Applied Sciences, Nibelungenplatz 1, Frankfurt am Main; D-60318, Germany^1;Plymouth University, Drake Circus, Plymouth, Devon; PL48AA, United Kingdom^2
[效力级别] 电工学 [学科分类]
[关键词] Aggregate load;Contextual information;Energy consumption datum;Energy use;Fitness functions;Non-intrusive appliance load monitoring;Priori information;Theoretical framework [时效性]
