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Modeling occupant behavior’s influence on the energy efficiency of solar domestic hot water systems

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  • Zhou, Xin
  • Tian, Shuai
  • An, Jingjing
  • Yan, Da
  • Zhang, Lun
  • Yang, Junyan

Abstract

Solar hot water (SHW) systems are widely used to save energy and reduce CO2 emissions. The domestic hot water (DHW) usage behavior of occupants has a significant influence on the energy consumption of SHW systems; the operation of SHW systems also affects the DHW usage of occupants. However, there is a lack of quantitative analysis for coordinating DHW behavior and the design and operation of SHW systems to maximize solar energy utilization and reduce energy consumption. Therefore, an information-driven stochastic DHW usage model that interacts with SHW systems is proposed. The optimal design and control strategies for SHW systems to match user behaviors were explored. Meanwhile, the effects of occupants’ sensitivity to water temperature and their energy-saving awareness on the energy performance of SHW systems were studied. The results showed that optimal design and control strategies based on the actual DHW profile can increase the annual solar energy utilization rate by 11.5%, and the corresponding electricity savings, CO2 emission reduction, and cost saving are 163.10 kWh, 145.52 kg, and 81.06 RMB, per household. The location of the reheater had a significant influence on the energy performance and optimal measures of the SHW systems. Moreover, based on technologies or policies to adjust hot water demand, the water supply capacity of SHW systems can be matched with the hot water demand, which can improve the efficiency of solar energy utilization, save 8.5% of electrical energy for reheating, and lead to a 107.55 kg CO2 reduction and 59.91 RMB cost saving per household per year.

Suggested Citation

  • Zhou, Xin & Tian, Shuai & An, Jingjing & Yan, Da & Zhang, Lun & Yang, Junyan, 2022. "Modeling occupant behavior’s influence on the energy efficiency of solar domestic hot water systems," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921017189
    DOI: 10.1016/j.apenergy.2021.118503
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    1. Tian, Yanpei & Liu, Xiaojie & Ghanekar, Alok & Zheng, Yi, 2021. "Scalable-manufactured metal–insulator–metal based selective solar absorbers with excellent high-temperature insensitivity," Applied Energy, Elsevier, vol. 281(C).
    2. Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
    3. Prakash, Jyoti & Roan, Daryn & Tauqir, Wajeha & Nazir, Hassan & Ali, Majid & Kannan, Arunachala, 2019. "Off-grid solar thermal water heating system using phase-change materials: design, integration and real environment investigation," Applied Energy, Elsevier, vol. 240(C), pages 73-83.
    4. Good, Nicholas & Zhang, Lingxi & Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2015. "High resolution modelling of multi-energy domestic demand profiles," Applied Energy, Elsevier, vol. 137(C), pages 193-210.
    5. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
    6. Ma, Tao & Li, Meng & Kazemian, Arash, 2020. "Photovoltaic thermal module and solar thermal collector connected in series to produce electricity and high-grade heat simultaneously," Applied Energy, Elsevier, vol. 261(C).
    7. Spur, Roman & Fiala, Dusan & Nevrala, Dusan & Probert, Doug, 2006. "Influence of the domestic hot-water daily draw-off profile on the performance of a hot-water store," Applied Energy, Elsevier, vol. 83(7), pages 749-773, July.
    8. Gao, J.T. & Xu, Z.Y. & Wang, R.Z., 2020. "Experimental study on a double-stage absorption solar thermal storage system with enhanced energy storage density," Applied Energy, Elsevier, vol. 262(C).
    9. Linas Gelažanskas & Kelum A. A. Gamage, 2015. "Forecasting Hot Water Consumption in Residential Houses," Energies, MDPI, vol. 8(11), pages 1-16, November.
    10. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    11. Tschopp, Daniel & Tian, Zhiyong & Berberich, Magdalena & Fan, Jianhua & Perers, Bengt & Furbo, Simon, 2020. "Large-scale solar thermal systems in leading countries: A review and comparative study of Denmark, China, Germany and Austria," Applied Energy, Elsevier, vol. 270(C).
    12. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
    Full references (including those not matched with items on IDEAS)

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    3. Tian, Shuai & Lu, Yuxin & Zhou, Xin & Zhang, Lun & An, Jingjing & Yan, Da & Shi, Xing & Jin, Xing, 2023. "A new perspective of solar hot water system operation optimization: Supply and demand matching," Renewable Energy, Elsevier, vol. 207(C), pages 89-104.
    4. Ding, Zhixiong & Wu, Wei & Huang, Si-Min & Huang, Hongyu & Bai, Yu & He, Zhaohong, 2023. "A novel compression-assisted energy storage heat transformer for low-grade renewable energy utilization," Energy, Elsevier, vol. 263(PA).

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