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.
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