AMPds: A public dataset for load disaggregation and eco-feedback research

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.

[1]  Jeannie R. Albrecht,et al.  Smart * : An Open Data Set and Tools for Enabling Research in Sustainable Homes , 2012 .

[2]  Naoki Tanaka,et al.  Nonintrusive Load-Shed Verification , 2011, IEEE Pervasive Computing.

[3]  Fred Popowich,et al.  The cognitive power meter: Looking beyond the smart meter , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[4]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[5]  Alex Rogers,et al.  Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types , 2012, AAAI.

[6]  Johnny Rodgers,et al.  Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback , 2011, IEEE Transactions on Visualization and Computer Graphics.

[7]  Anthony Rowe,et al.  BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research , 2012 .

[8]  Jane Yung-jen Hsu,et al.  Applying power meters for appliance recognition on the electric panel , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[9]  Fred Popowich,et al.  Home Occupancy Agent: Occupancy and Sleep Detection , 2014 .

[10]  M. Newborough,et al.  Energy-use information transfer for intelligent homes : Enabling energy conservation with central and local displays , 2007 .

[11]  F. Sultanem,et al.  Using appliance signatures for monitoring residential loads at meter panel level , 1991 .

[12]  Ralf Steinmetz,et al.  On the accuracy of appliance identification based on distributed load metering data , 2012, 2012 Sustainable Internet and ICT for Sustainability (SustainIT).

[13]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[14]  James A. Landay,et al.  The design of eco-feedback technology , 2010, CHI.

[15]  Johnny Rodgers,et al.  Chasing the Negawatt: Visualization for Sustainable Living , 2010, IEEE Computer Graphics and Applications.

[16]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[17]  Stephen Makonin,et al.  The Affect of Lifestyle Factors on Eco-Visualization Design , 2014, ArXiv.

[18]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[19]  Silvia Santini,et al.  Towards automatic classification of private households using electricity consumption data , 2012, BuildSys@SenSys.

[20]  Steven Reece,et al.  Recommending energy tariffs and load shifting based on smart household usage profiling , 2013, IUI '13.

[21]  Michael Zeifman,et al.  Disaggregation of home energy display data using probabilistic approach , 2012, IEEE Transactions on Consumer Electronics.

[22]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.