IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v323y2022ics0306261922008844.html
   My bibliography  Save this article

Analysis of precooling optimization for residential buildings

Author

Listed:
  • Wang, Junke
  • Yik Tang, Choon
  • Song, Li

Abstract

To reduce peak demand and/or energy cost for residential buildings, optimal precooling strategies are becoming important as an alternative to rule-based precooling strategies that are intuitive but may not be optimal. Since precooling optimization is heavily dependent on a variety of factors such as the home thermal properties, HVAC system, weather, thermal comfort criteria, and utility rate structure, the individual and collective impact of these factors on precooling performance needs to be analyzed. In addition, since the indoor air temperature is affected by heat transfer to and from the interior wall surface, performance analysis in view of the interior wall surface temperature is also essential. Therefore, in this paper, an optimal precooling strategy that accounts for the aforementioned factors and utilizes a second-order thermal network model, is proposed. With this strategy, the HVAC on/off control signal that minimizes 24-hour energy cost while maintaining thermal comfort, is determined. Through extensive simulations, it is found that the proposed optimal precooling strategy is able to adapt to changing conditions and that having a sufficiently low interior wall surface temperature during precooling is critical for avoiding expensive on-peak operation. The reason for the latter is that such a temperature indicates that enough “cooling energy” has been stored. It is also found that weather has the most dominant impact on the precooling performance, followed by home thermal condition, with the rated cooling capacity and utility rate structure having the least impact.

Suggested Citation

  • Wang, Junke & Yik Tang, Choon & Song, Li, 2022. "Analysis of precooling optimization for residential buildings," Applied Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922008844
    DOI: 10.1016/j.apenergy.2022.119574
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922008844
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119574?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nelson, James & Johnson, Nathan G. & Chinimilli, Prudhvi Tej & Zhang, Wenlong, 2019. "Residential cooling using separated and coupled precooling and thermal energy storage strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Wang, Junke & Jiang, Yilin & Tang, Choon Yik & Song, Li, 2022. "Development and validation of a second-order thermal network model for residential buildings," Applied Energy, Elsevier, vol. 306(PB).
    3. Li, Xiwang & Malkawi, Ali, 2016. "Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions," Energy, Elsevier, vol. 112(C), pages 1194-1206.
    4. Tabares-Velasco, Paulo Cesar & Speake, Andrew & Harris, Maxwell & Newman, Alexandra & Vincent, Tyrone & Lanahan, Michael, 2019. "A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing," Applied Energy, Elsevier, vol. 242(C), pages 1346-1357.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adrian Chojecki & Arkadiusz Ambroziak & Piotr Borkowski, 2023. "Fuzzy Controllers Instead of Classical PIDs in HVAC Equipment: Dusting Off a Well-Known Technology and Today’s Implementation for Better Energy Efficiency and User Comfort," Energies, MDPI, vol. 16(7), pages 1-21, March.
    2. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort," Energy, Elsevier, vol. 289(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dehwah, Ammar H.A. & Krarti, Moncef, 2022. "Optimal controls of precooling strategies using switchable insulation systems for commercial buildings," Applied Energy, Elsevier, vol. 320(C).
    2. Sun, Yue & Luo, Zhiwen & Li, Yu & Zhao, Tianyi, 2024. "Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm," Energy, Elsevier, vol. 296(C).
    3. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).
    4. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    5. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    6. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
    7. Piotr Michalak, 2023. "Simulation and Experimental Study on the Use of Ventilation Air for Space Heating of a Room in a Low-Energy Building," Energies, MDPI, vol. 16(8), pages 1-17, April.
    8. Zhang, Wei & Valencia, Andrea & Gu, Lixing & Zheng, Qipeng P. & Chang, Ni-Bin, 2020. "Integrating emerging and existing renewable energy technologies into a community-scale microgrid in an energy-water nexus for resilience improvement," Applied Energy, Elsevier, vol. 279(C).
    9. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    10. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
    11. Byung-Ki Jeon & Eui-Jong Kim, 2022. "White-Model Predictive Control for Balancing Energy Savings and Thermal Comfort," Energies, MDPI, vol. 15(7), pages 1-12, March.
    12. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    13. Ligai Kang & Hao Li & Zhichao Wang & Jinzhu Wang & Dongxiang Sun & Yang Yang, 2023. "Investigation of Energy Consumption via an Equivalent Thermal Resistance-Capacitance Model for a Northern Rural Residence," Energies, MDPI, vol. 16(23), pages 1-18, November.
    14. Tabares-Velasco, Paulo Cesar & Speake, Andrew & Harris, Maxwell & Newman, Alexandra & Vincent, Tyrone & Lanahan, Michael, 2019. "A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing," Applied Energy, Elsevier, vol. 242(C), pages 1346-1357.
    15. Giovanni Bianco & Stefano Bracco & Federico Delfino & Lorenzo Gambelli & Michela Robba & Mansueto Rossi, 2020. "A Building Energy Management System Based on an Equivalent Electric Circuit Model," Energies, MDPI, vol. 13(7), pages 1-23, April.
    16. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort," Energy, Elsevier, vol. 289(C).
    17. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    18. Yucheng Guo & Jie Shi & Tong Guo & Fei Guo & Feng Lu & Lingqi Su, 2024. "Grey-Box Method for Urban Building Energy Modelling: Advancements and Potentials," Energies, MDPI, vol. 17(21), pages 1-25, October.
    19. Wu, Jian & Wang, Xiangyu & Li, Liang & Qin, Cun'an & Du, Yongchang, 2018. "Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control," Energy, Elsevier, vol. 145(C), pages 301-312.
    20. Ghorbani, Narges & Kasaeian, Alibakhsh & Toopshekan, Ashkan & Bahrami, Leyli & Maghami, Amin, 2018. "Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability," Energy, Elsevier, vol. 154(C), pages 581-591.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922008844. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.