Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine
International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Institute of Mathematics for Industry (IMI), Kyushu University, Motooka 744, Nishi-ku, Fukuoka 819-0395, Japan
Academic Editor: Mouloud Denai
Sustainability 2021, 13(15), 8321; https://doi.org/10.3390/su13158321
Received: 29 June 2021 / Revised: 21 July 2021 / Accepted: 22 July 2021 / Published: 26 July 2021
(This article belongs to the Special Issue Sustainable Technologies and Developments for Future Energy Systems)
The occupancy of residential energy consumers is an important subject to be studied to account for the changes on the load curve shape caused by paradigm shifts to consumer-centric energy markets or by significant energy demand variations due to pandemics, such as COVID-19. For non-intrusive occupancy analysis, multiple types of sensors can be installed to collect data based on which the consumer occupancy can be learned. However, the overall system cost will be increased as a result. Therefore, this research proposes a cheap and lightweight machine learning approach to predict the energy consumer occupancy based solely on their electricity consumption data. The proposed approach employs a support vector machine (SVM), in which different kernels are used and compared, including positive semi-definite and conditionally positive definite kernels. Efficiency of the proposed approach is depicted by different performance indexes calculated on simulation results with a realistic, publicly available dataset. Among SVM models with different kernels, those with Gaussian (rbf) and sigmoid kernels have the highest performance indexes, hence they may be most suitable to be used for residential energy consumer occupancy prediction.
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Keywords:
energy consumer occupancy; consumer-centric energy systems and approaches; support vector machine; machine learning; artificial intelligence
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MDPI and ACS Style
Nguyen, D.H. Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine. Sustainability 2021, 13, 8321. https://doi.org/10.3390/su13158321
AMA Style
Nguyen DH. Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine. Sustainability. 2021; 13(15):8321. https://doi.org/10.3390/su13158321
Chicago/Turabian StyleNguyen, Dinh H. 2021. "Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine" Sustainability 13, no. 15: 8321. https://doi.org/10.3390/su13158321
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