Energy Monitoring with Self-Taught Deep Networks

location_city Sydney schedule Sep 18th 01:30 - 02:00 PM place Grand Lodge people 81 Interested

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.


Target Audience

Anyone interested in energy disaggregation and deep networks



schedule Submitted 4 years ago