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Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine

Sustainability, 2021-08, Vol.13 (15), p.8321 [Tạp chí có phản biện]

2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su13158321

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  • Nhan đề:
    Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine
  • Tác giả: Nguyen, Dinh Hoa
  • Chủ đề: Classification ; Consumers ; COVID-19 ; Data collection ; Datasets ; Electricity ; Electricity consumption ; Energy consumption ; Energy demand ; Kernels ; Lagrange multiplier ; Learning algorithms ; Machine learning ; Neural networks ; Occupancy ; Optimization ; Performance indices ; Residential energy ; Sensors ; Smart grid technology ; Support vector machines ; Sustainability
  • Là 1 phần của: Sustainability, 2021-08, Vol.13 (15), p.8321
  • Mô tả: 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.
  • Nơi xuất bản: Basel: MDPI AG
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 2071-1050
    EISSN: 2071-1050
    DOI: 10.3390/su13158321
  • Nguồn: GFMER Free Medical Journals
    Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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