Non-Intrusive Appliance Load Monitoring using Genetic Algorithms

2018 3rd Asia Conference on Power and Electrical Engineering

能源学;电工学

Hock, D.^1 ; Kappes, M.^1 ; Ghita, B.^2

Frankfurt University of Applied Sciences, Nibelungenplatz 1, Frankfurt am Main

D-60318, Germany^1

Plymouth University, Drake Circus, Plymouth, Devon

PL48AA, United Kingdom^2

关键词: Aggregate load;    Contextual information;    Energy consumption datum;    Energy use;    Fitness functions;    Non-intrusive appliance load monitoring;    Priori information;    Theoretical framework;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/366/1/012003/pdf
DOI  :  10.1088/1757-899X/366/1/012003

来源: IOP

【 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.