Sensors & Behavior (ARPA-E) > Technology Projects

Disaggregation Algorithms

Algorithmic approaches to energy disaggregation have traditionally been very simple, and focused solely on detecting changes in a limited number of device states, (e.g., off, on high, on low, sleep mode), in a power signal.




Performance of different disaggregation algorithms on high-frequency data.

Investigators: J. Zico Kolter, Andrew Y. Ng (PI), Siddarth Batra, Tommi Jaakkola, Matthew J. Johnson

Such detection alone does not give a breakdown of power consumption in homes and is fundamentally limited to monitoring frequencies where such events are obvious. The methods would be unusable, for example, with just hourly data from smart meters. This project developed new algorithmic techniques that can breakdown electricity use more accurately than previous approaches, using data at a variety of different time scales. The researchers accomplished this goal through two studies: the first developed models of typical fingerprints for numerous types of appliances and devices; the second study used these models to develop algorithms to break down consumption from the aggregate signal.

The first study recorded power each hour on average for about 10,000 individually monitored devices to develop models for appliances and separate the signals. Researchers examined a fixed period of time, (in this study, one week), and expressed each appliance's entire energy trace over that time in terms of some linear combination of basis functions, which capture typical usage patterns. Activations specify which of these basis functions makes up any given signal. Given a collection of example usage patterns for an individual device, the study learned both the bases and activations using a method known as sparse coding. Researchers also developed additional algorithmic extensions that tailor sparse coding to this source separation setting.

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Publications and Presentations

Approximate inference in additive factorial HMMs with application to energy disaggregation (745KB PDF)
In International Conference on Artificial Intelligence and Statistics (pp. 1472-1482)
Kolter, J. Z., & Jaakkola, T. (2012)

REDD: A public data set for energy disaggregation research (893KB PDF)
In proceedings of the SustKDD workshop on Data Mining Applications in Sustainability (pp. 1-6)
Kolter, J. Z., & Johnson, M. J. (2011)

Energy disaggregation via discriminative sparse coding (274KB PDF)
In Advances in Neural Information Processing Systems (pp. 1153-1161)
Kolter, J. Z., Batra, S., & Ng, A. (2010)

Future Work

Next steps and future/ongoing work for these approaches include: developing methods that can build models using aggregate data alone, rather than both aggregate and individual device level; combining small numbers homes monitored at high frequency with large data sets of smart meter data; integrating the approaches into deployed systems in building energy management solutions.