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We propose a novel approach to enable Automated Machine Learning (AutoML) forNon-Intrusive Appliance Load Monitoring (NIALM), also known as EnergyDisaggregation, through Bayesian Optimization. NIALM offers a cost-effectivealternative to smart meters for measuring the energy consumption of electricdevices and appliances. NIALM methods analyze the entire power consumptionsignal of a household and predict the type of appliances as well as theirindividual power consumption (i.e., their contributions to the aggregatedsignal). We enable NIALM domain experts and practitioners who typically have nodeep data analytics or Machine Learning (ML) skills to benefit fromstate-of-the-art ML approaches to NIALM. Further, we conduct a survey andbenchmarking of the state of the art and show that in many cases, simple andbasic ML models and algorithms, such as Decision Trees, outperform the state ofthe art. Finally, we present our open-source tool, AutoML4NIALM, which willfacilitate the exploitation of existing methods for NIALM in the industry.

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