This site is dedicated to NILM and disaggregation research done by Stephen Makonin.

Nonintrusive (Appliance) Load Monitoring (NILM or NIALM), sometimes called load disaggregation, is an area in computational sustainability that tries to discern what electrical loads (e.i. appliance) are running within a physical area where power is supplied to from the main power meter. Such areas can include communities, industrial sites, office towers, buildings, homes, and even with in an appliance. By knowing what loads are running, when they are running, and how much power they are consuming we can begin to make informed choices that can lead to a reduction in power consumption.

It is common knowledge that our increase in consumption is neither economically nor environmentally sustainable. Power utilities around the world are or will be bringing in time-of-day usage tariffs These tariffs try to discourage power consumption during peak hours of usage and defer that consumption to off-peak hours (a.k.a. peak shaving). Peak shaving is a response to the rolling blackouts caused by demand exceeding supply. When this happens, the power grid experiences blackouts which is an inconvenience to all. This is only compounded as the demand for power increases. Residential homes consume about 34% of the total power consumption in the USA and are projected to increase to 39% by 2030. Economically this means consumers will be charged higher prices and environmentally this means producing more supply using unclean sources (e.g. coal).

Talks & Presentations

The playlist from NILM 2016 that I organized and hosted In Vancouver, Canada, May 14-15

I gave and inveited talk at the 2nd EU NILM Workshop On July 8, 2015 at Imperial College London.

Open Source Projects

The following are my NILM projects that have been released as open source on GitHub.

Sparse NILM
A super-state hidden Markov model disaggregator that uses a sparse Viterbi algorithm for decoding written about in the IEEE Transactions on Smart Grid journal paper. If you use this code please cite my journal paper.
ALIP NILM
An aided linear integer programming (ALIP) non-intrusive load monitoring (NILM) algorithm. Currently using in a pre-prints paper on arXiv.org. If you use this code please cite that paper.
Data Wrangle REDD
Importing script to data wrangle (convert, clean, and repair data from) the REDD dataset. If you use this code please cite my PhD thesis.
NILM Performance Evaluation
NILM performance evaluation functions use in the Springer Energy Efficiency journal paper. If you use this code please cite my journal paper.
Ammeter
A shield for the Arduino Due to mesure current using current transformers (CT) for up to 4 power lines. This ammeter was used to run my PhD thesis disaggregation algorithm called µDisagg. If you use this code please cite my APPEEC 2013 paper.

Open Datasets

The following are my NILM datasets that have been released as open to the public.

AMPds2: The Almanac of Minutely Power dataset (Version 2)
Version 2 of the AMPds dataset has been release to help load disaggregation/NILM and eco-feedback researcher test their algorithms, models, systems, and prototypes. This dataset is intended to be multi-year capture of the consumption of my house. This dataset contains electricity, water, and natural gas measurements at one minute intervals. This dataset contains a total of 1,051,200 readings for 2 years of monitoring (from April/2012 to March/2014) per meter. There are a total of 21 power meters, 2 water meters (with additional appliance usage annotations), and 2 natural gas meters. Weather data from Environment Canada's YVR weather station has also been added. This hourly weather data covers the same period of time as AMPds and includes a summary of climate normals observed from the years between 1981-2010. Billing data from utility companies is also included for cost/benefit analysis.
AMPds: Almanac of Minutely Power dataset (R2013)
The AMPds dataset has been released to help load disaggregation/NILM and eco-feedback researcher test their algorithms, models, systems, and prototypes. This is the original version, Release 2013 (R2013). This dataset contains electricity, water, and natural gas measurements at one-minute intervals. A total of 525,600 readings per meter.
ODDs: Occupancy Detection Dataset
This dataset contains power meter, ambient light, and ambient temperature sensors readings; including weather and daylight data. Sensor readings are at 15 minute intervals for about 8 months. This dataset was originally used in the my conference publication and the later expanded journal paper. This dataset monitors the same house that AMPds and should have some limited use for testing very low-frequency load disaggregation (nonintrusive load monitoring or NILM) algorithms and activity inference algorithms.

Published Research Papers

The following are my NILM peer-reviewed journal and conference papers.

  1. Md. A. Z. Bhotto, S. Makonin, and I. V. Bajic (2016). Load disaggregation based on aided linear integer programming. Transactions on Circuits and Systems II: Express Briefs, vol. PP, no. 99, pp. 1-5, IEEE.
  2. S. Makonin, B. Ellert, I. V. Bajic, and F. Popowich (2016). Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014.. Scientific Data, vol. 3, no. 160037 , pp. 1-12, NPG.
  3. S. Makonin (2016). Investigating the Switch Continuity Principle Assumed in Non-Intrusive Load Monitoring (NILM). In Proceedings of the 29th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).
  4. S. Makonin, F. Popowich, I. V. Bajic, B. Gill, and L. Bartram (2015). Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring (NILM). Transactions on Smart Grid, Volume PP, Issue 99, Pages 1-11, IEEE.
  5. B. Ellert, S. Makonin, and F. Popowich (2015). Appliance Water Disaggregation via Non-Intrusive Load Monitoring (NILM). Proceedings of the EAI International Conference on Big Data and Analytics for Smart Cities (BigDASC).
  6. S. Makonin and F. Popowich (2015). Nonintrusive Load Monitoring (NILM) Performance Evaluation: A unified approach for accuracy reporting. Energy Efficiency, Volume 8, Issue 4, Pages 809–814, Springer.
  7. S. Makonin (2014). Real-Time Embedded Low-Frequency Load Disaggregation. PhD thesis, Simon Fraser University, School of Computing Science.
  8. S. Makonin, I. V. Bajic, and F. Popowich (2014). Efficient Sparse Matrix Processing for Nonintrusive Load Monitoring (NILM). In Proceedings of the 2nd International Workshop on Non- Intrusive Load Monitoring.
      
  9. S. Makonin, L. Guzman Flores, R. Gill, R. A. Clapp, L. Bartram, and B. Gill (2014). A Consumer Bill of Rights for Energy Conservation. In Proceedings of the 2014 IEEE Canada International Humanitarian Technology Conference (IHTC).
  10. S. Makonin (2014). Nonintrusive Load Monitoring (NILM): What an algorithm can tell you about your energy consumption. In Poster Session at IEEE Vancouver Section Annual General Meeting.
  11. S. Makonin, W. Sung, R. Dela Cruz, B. Yarrow, B. Gill, F. Popowich, and I. V. Bajic (2013). Inspiring Energy Conservation through Open Source Metering Hardware and Embedded Real-Time Load Disaggregation. In Proceedings of the 5th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).
  12. S. Makonin, F. Popowich, L. Bartram, B. Gill, and I. V. Bajic (2013). AMPds: A Public Dataset for Load Disaggregation and Eco-Feedback Research. In Proceedings of the 2013 IEEE Electrical Power and Energy Conference (EPEC).
  13. S. Makonin, F. Popowich, and B. Gill (2013). The Cognitive Power Meter: Looking Beyond the Smart Meter. In Proceedings of the 2013 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).
  14. S. Makonin, (2012). Approaches to Non-Intrusive Load Monitoring (NILM) in the Home. PhD Depth Report, Simon Fraser University, School of Computing Science.