Energy Disaggregation using Self-Taught Deep Networks
Energy disaggregation allows detection of individual electrical appliances from aggregated energy usage time series data. The insights of individual appliances are very useful for different energy-related applications, for example energy monitoring, demand response etc. Although it is very easy to collect large volume of energy usage data, inspecting and labelling time series is very tedious and expensive. In this talk, I will present a solution to explore these unlabelled time-series data using two deep networks. The first RNN-based deep network extracts good representations of energy time series windows without much human intervention. By transferring these representations from unlabelled data to labeled data, the second deep network learns the model of targeted electrical appliance.
Outline/structure of the Session
Anyone interested in energy disaggregation and deep networks