Structured dictionary learning for energy disaggregation

Shalini Pandey, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


The increased awareness regarding the impact of energy consumption on the environment has led to an increased focus on reducing energy consumption. Feedback on the appliance level energy consumption can help in reducing the energy demands of the consumers. Energy disaggregation techniques are used to obtain the appliance level energy consumption from the aggregated energy consumption of a house. These techniques extract the energy consumption of an individual appliance as features and hence face the challenge of distinguishing two similar energy consuming devices. To address this challenge we develop methods that leverage the fact that some devices tend to operate concurrently at specific operation modes. The aggregated energy consumption patterns of a subgroup of devices allows us to identify the concurrent operating modes of devices in the subgroup. Thus, we design hierarchical methods to replace the task of overall energy disaggregation among the devices with a recursive disaggregation task involving device subgroups. Experiments on two real-world datasets show that our methods lead to improved performance as compared to baseline. One of our approaches, Greedy based Device Decomposition Method (GDDM) achieved up to 23.8%, 10% and 59.3% improvement in terms of micro-averaged f score, macro-averaged f score and Normalized Disaggregation Error (NDE), respectively.

Original languageEnglish (US)
Title of host publicatione-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages11
ISBN (Electronic)9781450366717
StatePublished - Jun 15 2019
Event10th ACM International Conference on Future Energy Systems, e-Energy 2019 - Phoenix, United States
Duration: Jun 25 2019Jun 28 2019

Publication series

Namee-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems


Conference10th ACM International Conference on Future Energy Systems, e-Energy 2019
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.

Publisher Copyright:
© 2019 Association for Computing Machinery.


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