Last week I learnt that that my full co-authored (with co-author Fred Popowich, Lyn Bartram, Bob Gill, and Ivan Bajic) paper “AMPds: A Public Dataset for Load Disaggregation and Eco-Feedback Research” was accepted at IEEE‘s Annual Electrical Power and Energy Conference (EPEC 2013). So I will be travelling to Halifax, Nova Scotia in the Canada. Here is the paper abstract:
A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.
Keywords: Power Meter, Current, Dataset, Load Disaggregation, Eco-Feedback, Single-Measurement, Maximum a Posteriori (MAP), Energy Conservation
The dataset (and paper) can be found at http://ampds.org.
Non-Intrusive Load Monitoring (NILM) 