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Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons

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  • Alibabaei, Nima
  • Fung, Alan S.
  • Raahemifar, Kaamran
  • Moghimi, Arash

Abstract

Based on their structure, residential houses/buildings (RHs) can offer excellent opportunities for managing their internal energy demand and subsequently lowering their energy cost. Demand management and energy cost saving can be achieved by taking advantage of RHs/buildings capabilities in storing thermal energy. Thermal energy can be stored utilizing intelligent Strategy Planning Models (SPMs) which are applied in the heating, ventilating and air conditioning (HVAC) system as one of the largest energy consumer in RHs buildings. This study discusses the development of three different strategy planning models including Smart Dual Fuel Switching System (SDFSS), Load Shifting (LSH), and LSHSDFSS, a combination of load shifting and fuel switching SPMs. In order to facilitate the implementation of the developed SPMs on the HVAC system of the house used in this case study, an advanced controller was designed by connecting both TRNSYS-Matlab programs. The HVAC system energy demand as well as the corresponding saving on the HVAC system energy cost are analyzed in-depth numerically using each of the strategy planning models during both the heating and cooling seasons. Simulation results showed that in the heating season, the operating/energy cost of HVAC system decreased significantly (23.8%) by implementing SDFSS-SPM. LSHSDFSS-SPM reduced the HVAC system operating cost by 15.8%. In the cooling season, LSH-SPM reduced the HVAC system operating cost by 6.63%.

Suggested Citation

  • Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p1:p:29-43
    DOI: 10.1016/j.apenergy.2016.10.062
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    3. Wangqi Xiong & Jiandong Wang, 2020. "Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching," Energies, MDPI, vol. 13(8), pages 1-18, April.
    4. Daehyun Kim & Hyunmuk Lim & Jongmin Moon & Jinsoo Park & Gwanghoon Rhee, 2021. "Heating Performances of a Large-Scale Factory Evaluated through Thermal Comfort and Building Energy Consumption," Energies, MDPI, vol. 14(18), pages 1-16, September.
    5. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    6. Romero Rodríguez, Laura & Sánchez Ramos, José & Álvarez Domínguez, Servando & Eicker, Ursula, 2018. "Contributions of heat pumps to demand response: A case study of a plus-energy dwelling," Applied Energy, Elsevier, vol. 214(C), pages 191-204.
    7. 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.
    8. Bloemendal, Martin & Jaxa-Rozen, Marc & Olsthoorn, Theo, 2018. "Methods for planning of ATES systems," Applied Energy, Elsevier, vol. 216(C), pages 534-557.
    9. Germán Campos Gordillo & Germán Ramos Ruiz & Yves Stauffer & Stephan Dasen & Carlos Fernández Bandera, 2020. "EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB ® and C#," Sustainability, MDPI, vol. 12(2), pages 1-14, January.
    10. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    11. Samy Faddel & Guanyu Tian & Qun Zhou, 2021. "Decentralized Management of Commercial HVAC Systems," Energies, MDPI, vol. 14(11), pages 1-18, May.
    12. Dimitra G. Kyriakou & Fotios D. Kanellos & George J. Tsekouras & Konstantinos A. Moungos, 2025. "Effective and Local Constraint-Aware Load Shifting for Microgrid-Based Energy Communities," Energies, MDPI, vol. 18(2), pages 1-24, January.
    13. Dimitra G. Kyriakou & Fotios D. Kanellos, 2022. "Optimal Operation of Microgrids Comprising Large Building Prosumers and Plug-in Electric Vehicles Integrated into Active Distribution Networks," Energies, MDPI, vol. 15(17), pages 1-26, August.

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