Non-Intrusive Load Monitoring (NILM), commonly known as energy disaggregation, aims to estimate the power consumption of individual appliances by analyzing a home's total electricity usage. This method provides a cost-effective alternative to installing dedicated smart meters for each appliance. In this paper, we introduce a novel framework that incorporates Automated Machine Learning (AutoML) into the NILM domain, utilizing Bayesian Optimization for automated model selection and hyperparameter tuning. This framework empowers domain practitioners to effectively apply machine learning techniques without requiring advanced expertise in data science or machine learning. To support further research and industry adoption, we present AutoML4NILM, a flexible and extensible open-source toolkit designed to streamline the deployment of AutoML solutions for energy disaggregation. Currently, this framework supports 11 algorithms, each with different hyperparameters; however, its flexible design allows for the extension of both the algorithms and their hyperparameters.

翻译：非侵入式负荷监测（Non-Intrusive Load Monitoring，NILM），通常称为能量分解，旨在通过分析家庭总用电量来估算单个电器的功耗。该方法为每个电器安装专用智能电表提供了一种经济高效的替代方案。本文提出了一种新颖的框架，将自动化机器学习（AutoML）引入NILM领域，利用贝叶斯优化实现自动化模型选择与超参数调优。该框架使领域从业者能够有效应用机器学习技术，而无需具备数据科学或机器学习方面的高级专业知识。为支持进一步的研究与行业应用，我们推出了AutoML4NILM——一个灵活可扩展的开源工具包，旨在简化能量分解中AutoML解决方案的部署。目前，该框架支持11种算法，每种算法具有不同的超参数；但其灵活的设计允许对算法及其超参数进行扩展。