Classification of home appliance by using Probabilistic KNN with sensor data

Seungjun Kang, Ji Won Yoon

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

4 Citations (Scopus)

Abstract

To date, many researchers have been conducted studies to control an electrical power to construct a smart home system which automatically manipulates individuals. One of the recent topics is NILM(Non-intrusive Load Monitoring) system to infer the devices states. In NILM, the approaches have been focused on dealing only with the feature of the electrical power signals to identify the states of the running devices. However, it is hard to classify all of devices with such traditional approaches. To solve and increase the accuracy, we propose a new method to infer the device states by electrical power signal from the home appliances and also sensor data including temperature and humidity. In this paper, we compare the performance among PKNN(Probabilistic K-Nearest Neighbor) and other algorithms. We apply the three methods in PKNN and analyze the comparison through AUC(Area Under the ROC). Finally, we can find the optimized parameters for accurate classification in each method.

Original languageEnglish
Title of host publication2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789881476821
DOIs
Publication statusPublished - 2017 Jan 17
Event2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
Duration: 2016 Dec 132016 Dec 16

Other

Other2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
CountryKorea, Republic of
CityJeju
Period16/12/1316/12/16

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

Cite this

Kang, S., & Yoon, J. W. (2017). Classification of home appliance by using Probabilistic KNN with sensor data. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820745] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820745