IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v269y2023ics036054422300141x.html
   My bibliography  Save this article

Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method

Author

Listed:
  • Zhang, Yan
  • Teoh, Bak Koon
  • Zhang, Limao

Abstract

Building energy consumption and GHG emission have significant influence on urban sustainable development. However, research works that investigate the temporal information in energy prediction are limited. This study bridged these gaps and assessed the energy use intensity (EUI) and GHG intensity (GHGI) based on three aspects by considering spatio-temporal dimensions. A GTWR model capturing both spatial and temporal information was employed to assess ten driving forces towards buildings EUI and GHGI. A case study in Seattle was used to demonstrate the effectiveness and validate the proposed approach. Results indicate that (1) The temporal heterogeneity has significant influence on EUI and GHGI, where the value of R2 in EUI experienced a remarkable improvement of 21.82% in the GTWR model compared to the GWR model, and that of for GHGI has been increased 13.92% from GWR model to GTWR model; (2) Buildings with large GFA was found to positively impact the EUI and GHGI, while the population under poverty and the number of floors have a negative impact. The novelty of this study lies in establishing a comprehensive framework for predicting energy usage with spatio-temporal information and quantifying the impacts of the ten driving forces at a regional level.

Suggested Citation

  • Zhang, Yan & Teoh, Bak Koon & Zhang, Limao, 2023. "Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method," Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s036054422300141x
    DOI: 10.1016/j.energy.2023.126747
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422300141X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.126747?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. Zafar, Muhammad Wasif & Shahbaz, Muhammad & Sinha, Avik & Sengupta, Tuhin & Qin, Quande, 2020. "How Renewable Energy Consumption Contribute to Environmental Quality? The Role of Education in OECD Countries," MPRA Paper 100259, University Library of Munich, Germany, revised 08 May 2020.
    2. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    3. Borck, Rainald, 2016. "Will skyscrapers save the planet? Building height limits and urban greenhouse gas emissions," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 13-25.
    4. Shimei Wu & Xinye Zheng & Chu Wei, 2017. "Measurement of inequality using household energy consumption data in rural China," Nature Energy, Nature, vol. 2(10), pages 795-803, October.
    5. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    6. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    7. Meng, Ting & Hsu, David & Han, Albert, 2017. "Estimating energy savings from benchmarking policies in New York City," Energy, Elsevier, vol. 133(C), pages 415-423.
    8. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    9. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    10. Cayla, Jean-Michel & Maizi, Nadia & Marchand, Christophe, 2011. "The role of income in energy consumption behaviour: Evidence from French households data," Energy Policy, Elsevier, vol. 39(12), pages 7874-7883.
    11. Lin, Jinyao & Lu, Siyan & He, Xiaoyu & Wang, Fang, 2021. "Analyzing the impact of three-dimensional building structure on CO2 emissions based on random forest regression," Energy, Elsevier, vol. 236(C).
    12. Daioglou, Vassilis & van Ruijven, Bas J. & van Vuuren, Detlef P., 2012. "Model projections for household energy use in developing countries," Energy, Elsevier, vol. 37(1), pages 601-615.
    13. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    14. Fuerst, Franz & Warren-Myers, Georgia, 2018. "Does voluntary disclosure create a green lemon problem? Energy-efficiency ratings and house prices," Energy Economics, Elsevier, vol. 74(C), pages 1-12.
    15. Liddle, Brantley, 2014. "Impact of population, age structure, and urbanization on carbon emissions/energy consumption: Evidence from macro-level, cross-country analyses," MPRA Paper 61306, University Library of Munich, Germany.
    16. 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.
    17. Mingfang Tang & Xiao Fu & Huiming Cao & Yuan Shen & Hongbing Deng & Gang Wu, 2016. "Energy Performance of Hotel Buildings in Lijiang, China," Sustainability, MDPI, vol. 8(8), pages 1-12, August.
    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. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    2. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    3. Zhu, Mengshu & Huang, Ying & Wang, Si-Nuo & Zheng, Xinye & Wei, Chu, 2023. "Characteristics and patterns of residential energy consumption for space cooling in China: Evidence from appliance-level data," Energy, Elsevier, vol. 265(C).
    4. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    5. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    6. Liu, Xue & Ding, Yong & Tang, Hao & Fan, Lingxiao & Lv, Jie, 2022. "Investigating the effects of key drivers on energy consumption of nonresidential buildings: A data-driven approach integrating regularization and quantile regression," Energy, Elsevier, vol. 244(PA).
    7. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    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. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    10. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    11. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    12. Opoku, Eric Evans Osei & Dogah, Kingsley E. & Aluko, Olufemi Adewale, 2022. "The contribution of human development towards environmental sustainability," Energy Economics, Elsevier, vol. 106(C).
    13. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    14. Thomas Wu & Bo Wang & Dongdong Zhang & Ziwei Zhao & Hongyu Zhu, 2023. "Benchmarking Evaluation of Building Energy Consumption Based on Data Mining," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    15. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    16. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    17. Rosenfelder, Markus & Wussow, Moritz & Gust, Gunther & Cremades, Roger & Neumann, Dirk, 2021. "Predicting residential electricity consumption using aerial and street view images," Applied Energy, Elsevier, vol. 301(C).
    18. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    19. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    20. Aleksandra Matuszewska-Janica & Dorota Żebrowska-Suchodolska & Mariola E. Zalewska & Urszula Ala-Karvia & Marta Hozer-Koćmiel, 2023. "Differences in the Structure of Household Electricity Prices in EU Countries," Energies, MDPI, vol. 16(18), pages 1-23, September.

    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:energy:v:269:y:2023:i:c:s036054422300141x. 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.journals.elsevier.com/energy .

    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.