A User-Centered Active Learning Approach for Appliance Recognition


Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called K-Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time.

Meeting Name

6th IEEE International Conference on Smart Computing, SMARTCOMP 2020 (2020: Sep. 14-17, Virtual)


Psychological Science


This work is supported by the National Institute for Food and Agriculture (NIFA) under the grant 2017-67008-26145, the NSF grant EPCN 1936131, and the NSF CAREER grant CPS-1943035.

Keywords and Phrases

Appliance Recognition; Labeler Abstention; Stream-Based Active Learning; User-Centered Machine Learning

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Document Type

Article - Conference proceedings

Document Version


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Publication Date

17 Sep 2020