论文标题
自动化机器学习：一项关于非侵入性设备负载监控的案例研究
Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring
论文作者
论文摘要
我们提出了一种新的方法，以通过贝叶斯优化的优化来实现非侵入设备负载监测（NIALM）（NIALM），也称为能量分解的方法。 NIALM提供了智能电表的具有成本效益的替代方法，用于测量电动设备和设备的能耗。 NIALM方法分析了家庭的整个功耗信号，并预测了设备的类型及其个人功耗（即它们对汇总信号的贡献）。我们使NIALM领域专家和从业人员能够没有深度数据分析或机器学习（ML）技能（ML）技能，从而从最先进的NIALM方法中受益。此外，我们对最新状态进行了调查和基准测试，并表明在许多情况下，简单和基本的ML模型和算法（例如决策树）都表现出色。最后，我们介绍了我们的开源工具Automl4Nialm，这将促进对行业中NIALM现有方法的开发。
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.
