IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v333y2023ics0306261922018700.html
   My bibliography  Save this article

An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting

Author

Listed:
  • Chen, Yibo
  • Gao, Junxi
  • Yang, Jianzhong
  • Berardi, Umberto
  • Cui, Guoyou

Abstract

Due to the increasing demands of energy conservation and emission reduction, passive buildings with extra-low energy consumption and near zero energy consumption have attracted more attentions as a promising sustainable building type. Due to passive techniques such as air-tightness, passive buildings have a special demand of mechanical fresh air systems after closing windows. So it is indispensable to explore elaborate control strategies for maximizing natural ventilation, for the purpose of postponing the use of fossil energy. By taking meteorological forecast parameters as the input, an hour-head model-based predictive control (MPC) strategy of natural ventilation was proposed in this paper. Specifically, simulated multi-dimensional datasets obtained by setting different control actions under the same disturbances were obtained as the database for data-driven prediction models, which take weather forecast parameters as the input and labels of thermal comfort levels and air volumes as the output. It has been verified that although both with extended working hours and more stable comfort levels, the MPC strategy with priority to thermal comfort is superior to that with priority to fresh air volume. Furthermore, when compared with the traditional control strategy, ventilation hours via this MPC strategy could be increased by 56.3% from 366 h to 572 h. With this MPC model, the self-adaptive adjustment of window openness could be achieved by taking hour-ahead weather forecasting parameters as the input, thus to facilitate the climatic self-adaptive intelligent operation of passive buildings in future.

Suggested Citation

  • Chen, Yibo & Gao, Junxi & Yang, Jianzhong & Berardi, Umberto & Cui, Guoyou, 2023. "An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting," Applied Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922018700
    DOI: 10.1016/j.apenergy.2022.120613
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922018700
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120613?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. Piselli, Cristina & Prabhakar, Mohit & de Gracia, Alvaro & Saffari, Mohammad & Pisello, Anna Laura & Cabeza, Luisa F., 2020. "Optimal control of natural ventilation as passive cooling strategy for improving the energy performance of building envelope with PCM integration," Renewable Energy, Elsevier, vol. 162(C), pages 171-181.
    2. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2020. "Data-driven evaluation of HVAC operation and savings in commercial buildings," Applied Energy, Elsevier, vol. 278(C).
    3. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
    4. Shamim, Jubair A. & Hsu, Wei-Lun & Paul, Soumyadeep & Yu, Lili & Daiguji, Hirofumi, 2021. "A review of solid desiccant dehumidifiers: Current status and near-term development goals in the context of net zero energy buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    5. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    6. Chenari, Behrang & Dias Carrilho, João & Gameiro da Silva, Manuel, 2016. "Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1426-1447.
    7. Yu, Min Gyung & Pavlak, Gregory S., 2022. "Extracting interpretable building control rules from multi-objective model predictive control data sets," Energy, Elsevier, vol. 240(C).
    8. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Moosavi, Leila & Mahyuddin, Norhayati & Ab Ghafar, Norafida & Azzam Ismail, Muhammad, 2014. "Thermal performance of atria: An overview of natural ventilation effective designs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 654-670.
    10. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
    11. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
    12. Elnour, Mariam & Himeur, Yassine & Fadli, Fodil & Mohammedsherif, Hamdi & Meskin, Nader & Ahmad, Ahmad M. & Petri, Ioan & Rezgui, Yacine & Hodorog, Andrei, 2022. "Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities," Applied Energy, Elsevier, vol. 318(C).
    13. Martinopoulos, Georgios & Papakostas, Konstantinos T. & Papadopoulos, Agis M., 2018. "A comparative review of heating systems in EU countries, based on efficiency and fuel cost," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 687-699.
    14. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2017. "Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology," Applied Energy, Elsevier, vol. 193(C), pages 276-286.
    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. Mingfei Wang & Wengang Zheng & Chunjiang Zhao & Yang Chen & Chunling Chen & Xin Zhang, 2023. "Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC," Energies, MDPI, vol. 16(22), pages 1-14, November.
    2. Lichen Su & Jinlong Ouyang & Li Yang, 2023. "Mixed-Mode Ventilation Based on Adjustable Air Velocity for Energy Benefits in Residential Buildings," Energies, MDPI, vol. 16(6), pages 1-17, March.

    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. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    2. Payam Nejat & Fatemeh Jomehzadeh & Hasanen Mohammed Hussen & John Kaiser Calautit & Muhd Zaimi Abd Majid, 2018. "Application of Wind as a Renewable Energy Source for Passive Cooling through Windcatchers Integrated with Wing Walls," Energies, MDPI, vol. 11(10), pages 1-23, September.
    3. Tien, Paige Wenbin & Wei, Shuangyu & Calautit, John Kaiser & Darkwa, Jo & Wood, Christopher, 2022. "Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand," Applied Energy, Elsevier, vol. 308(C).
    4. Jomehzadeh, Fatemeh & Nejat, Payam & Calautit, John Kaiser & Yusof, Mohd Badruddin Mohd & Zaki, Sheikh Ahmad & Hughes, Ben Richard & Yazid, Muhammad Noor Afiq Witri Muhammad, 2017. "A review on windcatcher for passive cooling and natural ventilation in buildings, Part 1: Indoor air quality and thermal comfort assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 736-756.
    5. Sakiyama, N.R.M. & Carlo, J.C. & Frick, J. & Garrecht, H., 2020. "Perspectives of naturally ventilated buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    6. Zhang, Haihua & Yang, Dong & Tam, Vivian W.Y. & Tao, Yao & Zhang, Guomin & Setunge, Sujeeva & Shi, Long, 2021. "A critical review of combined natural ventilation techniques in sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    7. Refaa Sokkar & Halil Z. Alibaba, 2020. "Thermal Comfort Improvement for Atrium Building with Double-Skin Skylight in the Mediterranean Climate," Sustainability, MDPI, vol. 12(6), pages 1-26, March.
    8. Janusz Marchwiński & Agnieszka Starzyk & Ołeksij Kopyłow & Karolina Kurtz-Orecka, 2023. "Impact of Atrium Glazing with and without BIPV on Energy Performance of the Low-Rise Building: A Central European Case Study," Energies, MDPI, vol. 16(12), pages 1-25, June.
    9. Du, Kun & Calautit, John & Eames, Philip & Wu, Yupeng, 2021. "A state-of-the-art review of the application of phase change materials (PCM) in Mobilized-Thermal Energy Storage (M-TES) for recovering low-temperature industrial waste heat (IWH) for distributed heat," Renewable Energy, Elsevier, vol. 168(C), pages 1040-1057.
    10. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    11. Mukhtar, A. & Ng, K.C. & Yusoff, M.Z., 2018. "Passive thermal performance prediction and multi-objective optimization of naturally-ventilated underground shelter in Malaysia," Renewable Energy, Elsevier, vol. 123(C), pages 342-352.
    12. David Vérez & Luisa F. Cabeza, 2021. "Which Building Services Are Considered to Have Impact on Climate Change?," Energies, MDPI, vol. 14(13), pages 1-16, June.
    13. Sanjeeta N. Ghimire & Garrett Dave Hunter, 2020. "Energy Consumption, Window Size, and Environment-Friendly Building Design: Analysis Using Scale Model Houses," Journal of Development Innovations, KarmaQuest International, vol. 4(1), pages 64-79, July.
    14. Mikhail Tokarev, 2019. "A Double-Bed Adsorptive Heat Transformer for Upgrading Ambient Heat: Design and First Tests," Energies, MDPI, vol. 12(21), pages 1-14, October.
    15. Zhikun Ding & Rongsheng Liu & Zongjie Li & Cheng Fan, 2020. "A Thematic Network-Based Methodology for the Research Trend Identification in Building Energy Management," Energies, MDPI, vol. 13(18), pages 1-33, September.
    16. Behzad Rismanchi & Juan Mahecha Zambrano & Bryan Saxby & Ross Tuck & Mark Stenning, 2019. "Control Strategies in Multi-Zone Air Conditioning Systems," Energies, MDPI, vol. 12(3), pages 1-14, January.
    17. Mukhtar, A. & Ng, K.C. & Yusoff, M.Z., 2018. "Design optimization for ventilation shafts of naturally-ventilated underground shelters for improvement of ventilation rate and thermal comfort," Renewable Energy, Elsevier, vol. 115(C), pages 183-198.
    18. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    19. Zheng, Xu & Zhang, Yu & Wan, Tinghao & Chen, Kang, 2023. "Experimental study on the performance of a novel superabsorbent polymer and activated carbon composite coated heat exchangers," Energy, Elsevier, vol. 281(C).
    20. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.

    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:333:y:2023:i:c:s0306261922018700. 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.