Machine learning applications to electrical time series data will have wide-ranging impacts in the near future. Electricity disaggregation holds the promise of reducing billions of dollars of electrical waste every year. In the power grid, automatic classification of disturbance events detected by phasor measurement units could prevent cascading blackouts before they occur. Additional applications include better market segmentation by utility companies, improved design of appliances, and reliable incorporation of renewable energy resources into the power grid. However, existing machine learning methods remain unimplemented in the real world because of limiting assumptions that hinder performance. My research contributions are summarized as follows: In electricity disaggregation, I introduced the first label correction approach for supervised training samples. For unsupervised disaggregation, I introduced event detection that does not require parameter tuning and appliance discovery that makes no assumptions on appliance types. These improvements produce better accuracy, faster computation, and more scalability than any previously introduced method and can be to applied natural gas disaggregation, water disaggregation, and other source separation domains. My current work challenges long-held assumptions in time series shapelets, a classification tool with applicability in electrical time series and dozens of additional domains.

Electricity disaggregation identifies individual appliances from one or more aggregate data streams and has immense potential to reduce residential and commercial electrical waste. Since supervised learning methods rely on meticulously labeled training samples that are expensive to obtain, unsupervised methods show the most promise for wide-spread application. However, unsupervised learning methods previously applied to electricity disaggregation suffer from critical limitations. This paper introduces the concept of iterative appliance discovery, a novel unsupervised disaggregation method that progressively identifies the "easiest to find" or "most likely" appliances first. Once these simpler appliances have been identified, the computational complexity of the search space can be significantly reduced, enabling iterative discovery to identify more complex appliances. We test iterative appliance discovery against an existing competitive unsupervised method using two publicly available datasets. Results using different sampling rates show iterative discovery has faster runtimes and produces better accuracy. Furthermore, iterative discovery does not require prior knowledge of appliance characteristics and demonstrates unprecedented scalability to identify long, overlapped sequences that other unsupervised learning algorithms cannot.

My research contributions are summarized as follows: In electricity disaggregation, I introduced the first label correction approach for supervised training samples. For unsupervised disaggregation, I introduced event detection that does not require parameter tuning and appliance discovery that makes no assumptions on appliance types. These improvements produce better accuracy, faster computation, and more scalability than any previously introduced method and can be applied to natural gas disaggregation, water disaggregation, and other source separation domains. My current work challenges long-held assumptions in time series shapelets, a classification tool with applicability in electrical time series and dozens of additional domains.