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In Situ Performance Analysis of Hybrid Fuel Heating System in a Net-Zero Ready House

Author

Listed:
  • Wanrui Qu

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Alexander Jordan

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Bowen Yang

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Yuxiang Chen

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

The global population’s growth and increased energy consumption have driven greenhouse gas (GHG) emissions. In Canada, the residential sector accounts for 17% of secondary energy use and 13% of GHG emissions. To mitigate GHG emissions, promoting renewable energy and efficient heating systems is crucial, especially in cold climates like Canada, where there is a heavy dependency on fossil fuels for space heating applications. A viable solution is hybrid fuel heating systems that combine electric-driven air-source heat pumps (ASHPs) with natural gas tankless water heaters (TWHs). This system can alternate its operation between the ASHP and TWH based on efficiency and real-time energy costs, reducing grid peak demand and enhancing resilience during power outages. Although lab experiments have shown its benefits, in situ performance lacks evaluation. This study analyzes the in situ energy performance of a net-zero ready house and its hybrid fuel heating system, assessing energy consumption, hourly space heating output, and system heating performance. HOT2000 is a robust simulation software designed for assessing energy consumption, space heating, cooling, and domestic hot water systems in residential buildings. An artificial neural network model was developed to predict the energy performance of the hybrid fuel system, which was used as a substitute for monitored data for evaluating the HOT2000’s simulation results under the same weather conditions. Therefore, this study proposes a comprehensive framework for the in situ performance analysis of hybrid fuel heating systems. This study then, using HOT2000 energy consumption results, evaluates the life cycle costs of the hybrid fuel system against conventional heating systems. Furthermore, this study proposes an economical control strategy using in situ data or manufacturer specifications.

Suggested Citation

  • Wanrui Qu & Alexander Jordan & Bowen Yang & Yuxiang Chen, 2024. "In Situ Performance Analysis of Hybrid Fuel Heating System in a Net-Zero Ready House," Sustainability, MDPI, vol. 16(3), pages 1-29, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:964-:d:1324628
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    References listed on IDEAS

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    1. Hou, Zhijian & Lian, Zhiwei & Yao, Ye & Yuan, Xinjian, 2006. "Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique," Applied Energy, Elsevier, vol. 83(9), pages 1033-1046, September.
    2. Sun, Mingyang & Djapic, Predrag & Aunedi, Marko & Pudjianto, Danny & Strbac, Goran, 2019. "Benefits of smart control of hybrid heat pumps: An analysis of field trial data," Applied Energy, Elsevier, vol. 247(C), pages 525-536.
    3. Li, Gang & Du, Yuqing, 2018. "Performance investigation and economic benefits of new control strategies for heat pump-gas fired water heater hybrid system," Applied Energy, Elsevier, vol. 232(C), pages 101-118.
    4. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    5. Jelena Tihana & Hesham Ali & Jekaterina Apse & Janis Jekabsons & Dmitrijs Ivancovs & Baiba Gaujena & Andrei Dedov, 2023. "Hybrid Heat Pump Performance Evaluation in Different Operation Modes for Single-Family House," Energies, MDPI, vol. 16(20), pages 1-17, October.
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