Learning from Non-experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes

Jackson Codispoti, Atieh R. Khamesi, Nelson Penn, Simone Silvestri, Eura Shin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

With the acceleration of Information and Communication Technologies and the Internet-of-Things paradigm, smart residential environments, also known as smart homes, are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition. Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances' electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert, always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this article, we develop a SAL algorithm, called K-Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness, representativeness, and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.

Original languageEnglish
Article number3491241
JournalACM Transactions on Cyber-Physical Systems
Volume6
Issue number2
DOIs
StatePublished - Apr 2022

Bibliographical note

Funding Information:
This work is supported by the National Institute for Food and Agriculture (NIFA) under Grant No. 2017-67008-26145, the NSF under Grant No. EPCN 1936131, and NSF CAREER Grant No. CPS-1943035. Authors’ addresses: J. Codispoti, N. Penn, S. Silvestri, and E. Shin, University of Kentucky, 329 Rose Street Lexington, KY 40506-0633, USA; emails: Jackson.Codispoti@uky.edu, nape226@g.uky.edu, silvestri@cs.uky.edu, eura.shin@uky.edu; A. R. Khamesi, University of Kentucky, 329 Rose Street Lexington, KY 40506-0633, USA; email: atieh.khamesi@uky.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 2378-962X/2022/04-ART16 $15.00 https://doi.org/10.1145/3491241

Publisher Copyright:
© 2022 Association for Computing Machinery.

Keywords

  • Appliance recognition
  • stream-based active learning
  • user behavior

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Learning from Non-experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes'. Together they form a unique fingerprint.

Cite this