Trainingless multi-objective evolutionary computing-based nonintrusive load monitoring: Part of smart-home energy management for demand-side management. (January 2021)

• Record Type: Journal Article
• Authors: Lin, Yu-Hsiu
• Is Part Of: Journal of building engineering. Volume 33(2021)
• Publication Date: 2021-01
• Subjects: Artificial intelligence -- Home energy management system -- Internet of things -- Multi-objective combinatorial optimization -- Nonintrusive load monitoring; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2020.101601
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Electricity is the most widely used form of energy in modern society. One method of satisfying the continuously increasing industrial, commercial, and residential electrical-energy demands of consumers in smart grids is to use an Internet-of-things (IoT) service-oriented electrical-energy management system (EMS) to intrusively monitor and manage electrical loads, which can effectively react to demand-response schemes for demand-side management (DSM). Nonintrusive load monitoring (NILM), a viable cost-effective load disaggregation technique, has recently gained considerable attention as a nonintrusive alternative to EMS in the research field of smart grids. This paper presents a smart IoT-oriented home EMS founded on trainingless multi-objective evolutionary computing -based NILM for DSM in a smart grid. Evolutionary computing-based NILM is considered and addressed as a multi-objective combinatorial optimization problem. The proposed NILM technique can determine the electrical appliances based on their individual electrical characteristics extracted from composite electrical-load consumption with no intrusive deployment of smart plugs or power meters. A fully nonintrusive NILM alternative is considered and proposed. In addition, this alternative is different from conventional NILM because conventional NILM considers artificial intelligence including artificial neural networks (NNs) and deep NN as load classifiers of NILM where training and retraining stages and a hyperparameter tuning procedure are required. The proposed smart IoT-oriented home EMS was experimentally investigated with the trainingless multi-objective evolutionary computing-based NILM in a real house environment. The experimental results confirm that the proposed methodology is feasible. Highlights: Training-less multi-objective evolutionary computing-based NILM is presented. A fully-nonintrusive process involves only considering recorded states of appliances. NILM in this paper is a multi-objective combinatorial optimization model.