Active Learning Framework for Non-Intrusive Load Monitoring: Preprint

Research output: Contribution to conferencePaper


Non-Intrusive Load Monitoring (NILM) is a set of techniques that estimate the electricity usage of individual appliances from power measurements taken at a limited number of locations in a building. One of the key challenges in NILM is having too much data without class labels yet being unable to label the data manually for cost or time constraints. This paper presents an active learning framework that helps existing NILM techniques to overcome this challenge. Active learning is an advanced machine learning method that interactively queries a user for the class label information. Unlike most existing NILM systems that heuristically request user inputs, the proposed method only needs minimally sufficient information from a user to build a compact and yet highly representative load signature library. Initial results indicate the proposed method can reduce the user inputs by up to 90% while still achieving similar disaggregation performance compared to a heuristic method. Thus, the proposed method can substantially reduce the burden on the user, improve the performance of a NILM system with limited user inputs, and overcome the key market barriers to the wide adoption of NILM technologies.
Original languageAmerican English
Number of pages7
StatePublished - 2016
Event3rd International Workshop on Non-Intrusive Load Monitoring - Vancouver, Canada
Duration: 14 May 201615 May 2016


Conference3rd International Workshop on Non-Intrusive Load Monitoring
CityVancouver, Canada

NREL Publication Number

  • NREL/CP-5500-66273


  • active learning
  • BLUED dataset
  • electric load disaggregation
  • event classification
  • home automation
  • machine learning


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