Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review
Abstract
Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics.
Keywords
Cite
@article{arxiv.2306.05017,
title = {Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review},
author = {Mohammad Irani Azad and Roozbeh Rajabi and Abouzar Estebsari},
journal= {arXiv preprint arXiv:2306.05017},
year = {2023}
}
Comments
6 pages, EEEIC 2023 conference