Energy Monitoring with Self-Taught Deep Networks
YOW! Data 2017
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.
Sau Sheong Chang
Sau Sheong has been doing software development for 22 years, mostly in web application development. He is active in the Ruby and Go developer communities have have contributed to open source projects and spoke at meetups and conferences. Sau Sheong has also published 4 programming-related books, mostly on Ruby and Go. He currently works for SPGroup (previously known as Singapore Power), based out of sunny Singapore and has in his career worked for PayPal, HP, Yahoo, and also ran a technology startup during the dot-com days.