A Review of NILM Applications with Machine Learning Approaches

计算机科学 鉴定(生物学) 智能电表 过程(计算) 智能电网 异常检测 机器学习 网格 领域(数学分析) 人工智能 能量(信号处理) 工程类 数学分析 统计 植物 几何学 数学 生物 电气工程 操作系统
作者
Maheesha Dhashantha Silva,Qi Liu
出处
期刊:Computers, materials & continua 卷期号:79 (2): 2971-2989
标识
DOI:10.32604/cmc.2024.051289
摘要

In recent years, Non-Intrusive Load Monitoring (NILM) has become an emerging approach that provides affordable energy management solutions using aggregated load obtained from a single smart meter in the power grid.Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less of a burden for the energy monitoring process.However, conducted research works have limitations for real-time implementation due to the practical issues.This paper aims to identify the contribution of ML approaches to developing a reliable Energy Management (EM) solution with NILM.Firstly, phases of the NILM are discussed, along with the research works that have been conducted in the domain.Secondly, the contribution of machine learning approaches in three aspects is discussed: Supervised learning, unsupervised learning, and hybrid modeling.It highlights the limitations in the applicability of ML approaches in the field.Then, the challenges in the realtime implementation are concerned with six use cases: Difficulty in recognizing multiple loads at a given time, cost of running the NILM system, lack of universal framework for appliance detection, anomaly detection and new appliance identification, and complexity of the electricity loads and real-time demand side management.Furthermore, options for selecting an approach for an efficient NILM framework are suggested.Finally, suggestions are provided for future research directions.
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