IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i11p1986-d964447.html
   My bibliography  Save this article

Multi-Agent-Based Model for the Urban Macro-Level Impact Factors of Building Energy Consumption on Different Types of Land

Author

Listed:
  • Shiyi Song

    (School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Hong Leng

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China)

  • Ran Guo

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China)

Abstract

Urban researchers pay more and more attention to building energy consumption from different perspectives to obtain the results of urban overall energy conservation. The research at the micro level has yielded abundant accomplishments, but the macro-level research that can support urban planning decision making is still in the exploration stage. In this study, a multi-agent-based model, including the main panel, building agent, resident agent, and household appliance agent, is established by using Anylogic software. The model integrates Harbin urban macro-level impact factors of building energy consumption by designing and linking three sub-models: an urban morphology sub-model, climate sub-model, and energy use behavior sub-model. In the end, this study explored the building energy-saving potential of different types of land under the impact of variable factors through urban morphology and climate simulation scenarios and discussed the related energy-saving strategies. Findings and suggestions include: (1) The impact of urban morphology on overall urban building energy consumption is mainly reflected in residential and commercial land. The land development intensity (building density, floor area ratio, and building height) control and the coordination of land type layout and configuration can help to reduce the building energy consumption. (2) The energy-saving potential of residential land is more evident under climate impact, and ecological means should be used to adjust the climate to reduce the building energy consumption on different lands. (3) From the methodology perspective, this model can well realize the integration of multiple impact factors at the macro-level of the city and the dynamic simulation of energy consumption. The research results are expected to provide quantitative support for creating a sustainable built environment for the city.

Suggested Citation

  • Shiyi Song & Hong Leng & Ran Guo, 2022. "Multi-Agent-Based Model for the Urban Macro-Level Impact Factors of Building Energy Consumption on Different Types of Land," Land, MDPI, vol. 11(11), pages 1-24, November.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:11:p:1986-:d:964447
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/11/1986/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/11/1986/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    3. Delmastro, Chiara & Gargiulo, Maurizio, 2020. "Capturing the long-term interdependencies between building thermal energy supply and demand in urban planning strategies," Applied Energy, Elsevier, vol. 268(C).
    4. Walter, Travis & Sohn, Michael D., 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database," Applied Energy, Elsevier, vol. 179(C), pages 996-1005.
    5. Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an urban energy demand forecasting system to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 110(C), pages 304-317.
    6. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    7. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    8. Allen-Dumas, Melissa R. & Rose, Amy N. & New, Joshua R. & Omitaomu, Olufemi A. & Yuan, Jiangye & Branstetter, Marcia L. & Sylvester, Linda M. & Seals, Matthew B. & Carvalhaes, Thomaz M. & Adams, Mark , 2020. "Impacts of the morphology of new neighborhoods on microclimate and building energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Ming Lu & Guofei Sun & Zichong He & Wen-Tsao Pan, 2022. "Analysis and Evaluation of Factors Influencing the Low-Carbon Effect of Urban High-Rise Settlement Planning Schemes Based on AHP-Fuzzy Comprehensive Evaluation Method," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, July.
    10. Alessandro Franco & Lorenzo Miserocchi & Daniele Testi, 2021. "HVAC Energy Saving Strategies for Public Buildings Based on Heat Pumps and Demand Controlled Ventilation," Energies, MDPI, vol. 14(17), pages 1-20, September.
    11. 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.
    12. Luis Martin Dibene-Arriola & Fátima Maciel Carrillo-González & Sandra Quijas & María Carolina Rodríguez-Uribe, 2021. "Energy Efficiency Indicators for Hotel Buildings," Sustainability, MDPI, vol. 13(4), pages 1-11, February.
    13. Shi-Yi Song & Hong Leng, 2020. "Modeling the Household Electricity Usage Behavior and Energy-Saving Management in Severely Cold Regions," Energies, MDPI, vol. 13(21), pages 1-22, October.
    14. Tellier, Luc-Normand, 2021. "Characterizing urban form by means of the Urban Metric System," Land Use Policy, Elsevier, vol. 111(C).
    15. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
    16. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    17. Alaia Sola & Cristina Corchero & Jaume Salom & Manel Sanmarti, 2018. "Simulation Tools to Build Urban-Scale Energy Models: A Review," Energies, MDPI, vol. 11(12), pages 1-24, November.
    18. Shiyi Song & Hong Leng & Han Xu & Ran Guo & Yan Zhao, 2020. "Impact of Urban Morphology and Climate on Heating Energy Consumption of Buildings in Severe Cold Regions," IJERPH, MDPI, vol. 17(22), pages 1-24, November.
    Full references (including those not matched with items on IDEAS)

    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. Shiyi Song & Hong Leng & Han Xu & Ran Guo & Yan Zhao, 2020. "Impact of Urban Morphology and Climate on Heating Energy Consumption of Buildings in Severe Cold Regions," IJERPH, MDPI, vol. 17(22), pages 1-24, November.
    2. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    3. Xavier Faure & Tim Johansson & Oleksii Pasichnyi, 2022. "The Impact of Detail, Shadowing and Thermal Zoning Levels on Urban Building Energy Modelling (UBEM) on a District Scale," Energies, MDPI, vol. 15(4), pages 1-18, February.
    4. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    6. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
    7. Shimoda, Yoshiyuki & Yamaguchi, Yohei & Iwafune, Yumiko & Hidaka, Kazuyoshi & Meier, Alan & Yagita, Yoshie & Kawamoto, Hisaki & Nishikiori, Soichi, 2020. "Energy demand science for a decarbonized society in the context of the residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    8. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    10. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    11. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    12. Amin, Amin & Mourshed, Monjur, 2024. "Community stochastic domestic electricity forecasting," Applied Energy, Elsevier, vol. 355(C).
    13. Mastrucci, Alessio & Marvuglia, Antonino & Leopold, Ulrich & Benetto, Enrico, 2017. "Life Cycle Assessment of building stocks from urban to transnational scales: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 316-332.
    14. Joanna Piotrowska-Woroniak & Tomasz Szul, 2022. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings," Energies, MDPI, vol. 15(23), pages 1-13, November.
    15. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    17. Stefano Converso & Paolo Civiero & Stefano Ciprigno & Ivana Veselinova & Saffa Riffat, 2023. "Toward a Fast but Reliable Energy Performance Evaluation Method for Existing Residential Building Stock," Energies, MDPI, vol. 16(9), pages 1-24, May.
    18. Li, Xian-Xiang, 2018. "Linking residential electricity consumption and outdoor climate in a tropical city," Energy, Elsevier, vol. 157(C), pages 734-743.
    19. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    20. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.

    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:gam:jlands:v:11:y:2022:i:11:p:1986-:d:964447. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.