标题： 自动化机器学习：非侵入式电器负荷监测案例研究 显示英文标题

标题： Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring

摘要： 我们提出了一种新方法，通过贝叶斯优化来实现非侵入式电器负荷监测（NIALM）的自动化机器学习（AutoML），也称为能源分解。 NIALM为测量电器和设备的能耗提供了一种成本效益高的替代方案，以替代智能电表。 NIALM方法分析家庭的整个电力消耗信号，并预测电器的类型及其各自的电力消耗（即它们对聚合信号的贡献）。 我们使NIALM领域的专家和从业者能够受益于最先进的机器学习方法，这些专家通常没有深入的数据分析或机器学习（ML）技能。 此外，我们对最新技术进行了调查和基准测试，并表明在许多情况下，简单的基本机器学习模型和算法，如决策树，表现优于最新技术。 最后，我们介绍了我们的开源工具AutoML4NIALM，这将有助于在工业中利用现有的NIALM方法。 显示英文摘要

摘要： We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM), also known as Energy Disaggregation, through Bayesian Optimization. NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances. NIALM methods analyze the entire power consumption signal of a household and predict the type of appliances as well as their individual power consumption (i.e., their contributions to the aggregated signal). We enable NIALM domain experts and practitioners who typically have no deep data analytics or Machine Learning (ML) skills to benefit from state-of-the-art ML approaches to NIALM. Further, we conduct a survey and benchmarking of the state of the art and show that in many cases, simple and basic ML models and algorithms, such as Decision Trees, outperform the state of the art. Finally, we present our open-source tool, AutoML4NIALM, which will facilitate the exploitation of existing methods for NIALM in the industry.