IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v262y2023ipas0360544222023507.html
   My bibliography  Save this article

Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence

Author

Listed:
  • Zhang, Yan
  • Teoh, Bak Koon
  • Wu, Maozhi
  • Chen, Jiayu
  • Zhang, Limao

Abstract

Energy consumption prediction is an integral part of planning and controlling energy used in the building sector which accounts for 40% of the global energy consumption and a significant portion of greenhouse gas emissions. However, very few studies focused on the combined effect of building characteristics, building geometry, and urban morphology on energy performance. Such a research gap is addressed in this study by developing an explainable deep learning model. Our model uses Light Gradient Boosting Machine integrated with the SHapley Additive exPlanation algorithm, so as to provide insights into the feasibility of using machine learning-based models for energy performance prediction of buildings. With the proposed eXplainable Artificial Intelligence model, this study successfully predicts energy usage and greenhouse gas emissions of residential buildings, as well as identifies the most influential variables and evaluates their relative importance. A case study based on Seattle's data is used to verify the proposed framework, and some conclusions can be drawn: (1) Urban morphology and building geometry have significant effects on evaluating the building energy consumption and greenhouse gas emissions, as the accuracy of predicted result improve 33.46% compared with only considering building characteristics; (2) The total gross floor area and natural gas are identified as the most influential factors for energy consumption and GHG emissions, respectively; (3) The proposed model is examined to be an accurate method with the R2 of 0.8435 on average, comparing with the other approaches, such as the eXtreme Gradient Boosting, Random Forest, and Support Vector Regression. The main contributions of this research lie in that (a) a comprehensive structure integrated with building characteristics, building geometry, and urban morphology is established to forecast the energy use and greenhouse gas emissions; (b) an explainable artificial intelligence model incorporated with the SHapley Additive exPlanation algorithm into Light Gradient Boosting Machine has been proved to achieve an accurate prediction of the energy performance of residential buildings.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023507
    DOI: 10.1016/j.energy.2022.125468
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222023507
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125468?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. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Delzendeh, Elham & Wu, Song & Lee, Angela & Zhou, Ying, 2017. "The impact of occupants’ behaviours on building energy analysis: A research review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1061-1071.
    3. Wei, Jin & Ni, Yang & Zhang, Yue-Jun, 2020. "The mitigation strategies for bottom environment of service-oriented public building from a micro-scale perspective: A case study in China," Energy, Elsevier, vol. 205(C).
    4. Zheng, Yuanfan & Weng, Qihao, 2019. "Modeling the effect of climate change on building energy demand in Los Angeles county by using a GIS-based high spatial- and temporal-resolution approach," Energy, Elsevier, vol. 176(C), pages 641-655.
    5. Guo, Kai & Zhang, Limao, 2022. "Adaptive multi-objective optimization for emergency evacuation at metro stations," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. 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.
    7. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    8. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
    9. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    10. 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.
    11. Hemsath, Timothy L. & Alagheband Bandhosseini, Kaveh, 2015. "Sensitivity analysis evaluating basic building geometry's effect on energy use," Renewable Energy, Elsevier, vol. 76(C), pages 526-538.
    12. 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.
    13. 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.
    14. Jong-Hwa Park & Gi-Hyoug Cho, 2016. "Examining the Association between Physical Characteristics of Green Space and Land Surface Temperature: A Case Study of Ulsan, Korea," Sustainability, MDPI, vol. 8(8), pages 1-16, August.
    15. Zhang, Limao & Lin, Penghui, 2021. "Multi-objective optimization for limiting tunnel-induced damages considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    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. Islam, Md. Monirul & Shahbaz, Muhammad & Ahmed, Faroque, 2024. "Robot race in geopolitically risky environment: Exploring the Nexus between AI-powered tech industrial outputs and energy consumption in Singapore," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    2. Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(C).
    3. Wang, Xiaolu & Tan, Yumin & Zhou, Guanhua & Jing, Guifei & John Francis, Emolu, 2024. "A framework for analyzing energy consumption in urban built-up areas based on single photonic radar and spatial big data," Energy, Elsevier, vol. 290(C).
    4. Chen, Wanhe & Yin, Yonggao & Zhao, Xingwang & Fan, Fangsu & Cao, Bowen & Ji, Qiang & Xu, Guoying, 2023. "Sepiolite based humidity-control coating specially for alleviate the condensation problem of radiant cooling panel," Energy, Elsevier, vol. 272(C).
    5. 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).
    6. Jiang, Lina & Gao, Yafeng & Zhuang, Chaoqun & Feng, Chi & Zhang, Xiaotong & Guan, Jingxuan, 2024. "Experiment verification and simulation optimization of phase change material cool roof in summer -- A case study of Chongqing, China," Energy, Elsevier, vol. 293(C).
    7. Chen, Hongyu & Shen, Geoffrey Qiping & Feng, Zongbao & Liu, Yang, 2024. "Optimization of energy-saving retrofit solutions for existing buildings: A multidimensional data fusion approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    8. Xu, Tong & Zhang, Yajing & Shi, Longyu & Feng, Yunshuang & Ke, Xinjue & Zhang, Chengliang, 2023. "A comprehensive evaluation framework of energy and resources consumption of public buildings: Case study, People's Bank of China," Applied Energy, Elsevier, vol. 351(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. 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).
    2. 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).
    3. 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.
    4. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
    5. 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).
    6. 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).
    7. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    8. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    9. 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).
    10. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    11. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    12. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
    13. 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.
    14. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    15. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    16. Shaoxiong Li & Le Liu & Changhai Peng, 2020. "A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges," Sustainability, MDPI, vol. 12(4), pages 1-36, February.
    17. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
    18. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    19. Cai, Wei & Wen, Xiaodong & Li, Chaoen & Shao, Jingjing & Xu, Jianguo, 2023. "Predicting the energy consumption in buildings using the optimized support vector regression model," Energy, Elsevier, vol. 273(C).
    20. Salah Bouktif & Ali Ouni & Sanja Lazarova-Molnar, 2022. "Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview," Energies, MDPI, vol. 15(5), pages 1-30, 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:energy:v:262:y:2023:i:pa:s0360544222023507. 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.