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An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management

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  • Song, Chunhe
  • Jing, Wei
  • Zeng, Peng
  • Rosenberg, Catherine

Abstract

Based on an extensive dataset containing aggregated hourly energy consumption readings of residents during March 2011 and October 2012 in South Ontario, Canada, this paper estimates the energy consumption of circulating pumps of residential swimming pools (CPRSP) non-intrusively, and quantifies the impact of CPRSP on the power system. The main challenges are that, first, widely used non-intrusive appliance load monitoring (NIALM) methods are not applicable to this work, due to the low sampling rate and the lack of the energy consumption pattern of CPRSP; second, temperature-based building energy disaggregation methods are not suitable for this work, as they highly depend on the accurate base load estimation and predefined parameters. To overcome these issues, in this paper, first it is found that, during the pool season, for homes with and without swimming pools, the ratio between their base loads is approximately equal to the ratio between their temperature-dependent energy consumptions, then a novel weighted difference change-point (WDCP) model has been proposed. The advantages of the WDCP model are that, on one hand, it doesn’t depend on the base load estimation and predefined parameters; on the other hand, it has no requirement on the data sampling rate and the prior information of energy consumption patterns of CPRSP. Based on the WDCP model it is shown that, the average hourly energy consumption of CPRSP is 0.7425kW, and the minimum and the maximum hourly energy consumptions are 0.5274kW at 9:00 and 0.9612kW at 17:00, respectively. At the peak hour 19:00, July 21, 2011, CPRSP contributes 20.36% energy consumption of homes with swimming pools, as well as 8.48% peak load of all neighborhoods. As a result, the peak load could be reduced by 8.48% if all CPRSP are stopped during the peak hour.

Suggested Citation

  • Song, Chunhe & Jing, Wei & Zeng, Peng & Rosenberg, Catherine, 2017. "An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management," Applied Energy, Elsevier, vol. 195(C), pages 1-12.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:1-12
    DOI: 10.1016/j.apenergy.2017.03.023
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    1. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Jing Bai & Chuang Tu & Jiming Bai, 2024. "Measuring and decomposing Beijing’s energy performance: an energy- and exergy-based perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17617-17633, July.
    3. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
    4. Dong, Jingya & Song, Chunhe & Liu, Shuo & Yin, Huanhuan & Zheng, Hao & Li, Yuanjian, 2022. "Decentralized peer-to-peer energy trading strategy in energy blockchain environment: A game-theoretic approach," Applied Energy, Elsevier, vol. 325(C).
    5. Olszewski, Pawel & Arafeh, Jamal, 2018. "Parametric analysis of pumping station with parallel-configured centrifugal pumps towards self-learning applications," Applied Energy, Elsevier, vol. 231(C), pages 1146-1158.
    6. Yiran Wang & Shuowei Jin & Ming Cheng, 2023. "A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection," Sustainability, MDPI, vol. 15(13), pages 1-22, June.

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