IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v333y2023ics0306261922017998.html
   My bibliography  Save this article

Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data

Author

Listed:
  • Mayer, Kevin
  • Haas, Lukas
  • Huang, Tianyuan
  • Bernabé-Moreno, Juan
  • Rajagopal, Ram
  • Fischer, Martin

Abstract

Current methods to determine the energy efficiency of buildings generally require on-site visits of certified energy auditors which makes the process slow, costly, and geographically incomplete. To accelerate the identification of promising retrofit targets on a large scale, we propose to estimate building energy efficiency from widely available and remotely sensed data sources only, namely street view, aerial view, footprint, and satellite-borne land surface temperature (LST) data. After collecting data for almost 40,000 buildings in the United Kingdom, we combine these data sources by training multiple end-to-end deep learning models with the objective to classify buildings as energy efficient (EU rating A–D) or inefficient (EU rating E–G). After evaluating the trained models quantitatively as well as qualitatively, we extend our analysis by studying the predictive power of each data source in an ablation study. We find that the end-to-end deep learning model trained on all four data sources achieves a macro-averaged F1 score of 64.64% and outperforms models trained on building energy consumption data by 9.78%. As industry experts use building energy consumption data to approximate a building’s energy efficiency prior to a potential on-site inspection, this work shows the potential and complementary nature of remotely sensed data in predicting energy efficiency and opens up new opportunities for future work to integrate additional data sources.

Suggested Citation

  • Mayer, Kevin & Haas, Lukas & Huang, Tianyuan & Bernabé-Moreno, Juan & Rajagopal, Ram & Fischer, Martin, 2023. "Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data," Applied Energy, Elsevier, vol. 333(C).
  • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922017998
    DOI: 10.1016/j.apenergy.2022.120542
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922017998
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.120542?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. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
    2. 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).
    3. Li, Y. & Kubicki, S. & Guerriero, A. & Rezgui, Y., 2019. "Review of building energy performance certification schemes towards future improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    4. Sun, Maoran & Han, Changyu & Nie, Quan & Xu, Jingying & Zhang, Fan & Zhao, Qunshan, 2022. "Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow," OSF Preprints g8p4f, Center for Open Science.
    5. Peter Berrill & Eric J. H. Wilson & Janet L. Reyna & Anthony D. Fontanini & Edgar G. Hertwich, 2022. "Author Correction: Decarbonization pathways for the residential sector in the United States," Nature Climate Change, Nature, vol. 12(11), pages 1068-1068, November.
    6. Mayer, Kevin & Rausch, Benjamin & Arlt, Marie-Louise & Gust, Gunther & Wang, Zhecheng & Neumann, Dirk & Rajagopal, Ram, 2022. "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D," Applied Energy, Elsevier, vol. 310(C).
    7. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
    8. Sebastian Krapf & Nils Kemmerzell & Syed Khawaja Haseeb Uddin & Manuel Hack Vázquez & Fabian Netzler & Markus Lienkamp, 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning," Energies, MDPI, vol. 14(13), pages 1-22, June.
    9. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    10. Peter Berrill & Eric J. H. Wilson & Janet L. Reyna & Anthony D. Fontanini & Edgar G. Hertwich, 2022. "Decarbonization pathways for the residential sector in the United States," Nature Climate Change, Nature, vol. 12(8), pages 712-718, August.
    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. Gertsvolf, David & Horvat, Miljana & Aslam, Danesh & Khademi, April & Berardi, Umberto, 2024. "A U-net convolutional neural network deep learning model application for identification of energy loss in infrared thermographic images," Applied Energy, Elsevier, vol. 360(C).
    2. María Paz Sáez-Pérez & Luisa María García Ruiz & Francesco Tajani, 2024. "Assessment of the Thermal Properties of Buildings in Eastern Almería (Spain) during the Summer in a Mediterranean Climate," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    3. Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024. "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers) 850, Bank of Italy, Economic Research and International Relations Area.
    4. Zhao, Zhigao & Chen, Fei & He, Xianghui & Lan, Pengfei & Chen, Diyi & Yin, Xiuxing & Yang, Jiandong, 2024. "A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system," Applied Energy, Elsevier, vol. 357(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. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    2. Zhang, Hongji & Ding, Tao & Sun, Yuge & Huang, Yuhan & He, Yuankang & Huang, Can & Li, Fangxing & Xue, Chen & Sun, Xiaoqiang, 2023. "How does load-side re-electrification help carbon neutrality in energy systems: Cost competitiveness analysis and life-cycle deduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    3. Wiethe, Christian & Wenninger, Simon, 2023. "The influence of building energy performance prediction accuracy on retrofit rates," Energy Policy, Elsevier, vol. 177(C).
    4. David Frantz & Franz Schug & Dominik Wiedenhofer & André Baumgart & Doris Virág & Sam Cooper & Camila Gómez-Medina & Fabian Lehmann & Thomas Udelhoven & Sebastian Linden & Patrick Hostert & Helmut Hab, 2023. "Unveiling patterns in human dominated landscapes through mapping the mass of US built structures," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Al Kez, Dlzar & Foley, Aoife & Abdul, Zrar Khald & Del Rio, Dylan Furszyfer, 2024. "Energy poverty prediction in the United Kingdom: A machine learning approach," Energy Policy, Elsevier, vol. 184(C).
    6. Alina Galimshina & Maliki Moustapha & Alexander Hollberg & Sébastien Lasvaux & Bruno Sudret & Guillaume Habert, 2024. "Strategies for robust renovation of residential buildings in Switzerland," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    7. Barbara Widera, 2024. "Energy and Carbon Savings in European Households Resulting from Behavioral Changes," Energies, MDPI, vol. 17(16), pages 1-36, August.
    8. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
    9. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    10. Yan, Ran & Ma, Minda & Zhou, Nan & Feng, Wei & Xiang, Xiwang & Mao, Chao, 2023. "Towards COP27: Decarbonization patterns of residential building in China and India," Applied Energy, Elsevier, vol. 352(C).
    11. Zhang, Shufan & Zhou, Nan & Feng, Wei & Ma, Minda & Xiang, Xiwang & You, Kairui, 2023. "Pathway for decarbonizing residential building operations in the US and China beyond the mid-century," Applied Energy, Elsevier, vol. 342(C).
    12. Parupudi, Ranga Vihari & Singh, Harjit & Kolokotroni, Maria, 2020. "Low Concentrating Photovoltaics (LCPV) for buildings and their performance analyses," Applied Energy, Elsevier, vol. 279(C).
    13. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    14. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    15. Salah Beni Hamed & Mouna Ben Hamed & Lassaad Sbita, 2022. "Robust Voltage Control of a Buck DC-DC Converter: A Sliding Mode Approach," Energies, MDPI, vol. 15(17), pages 1-21, August.
    16. Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    17. Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024. "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers) 850, Bank of Italy, Economic Research and International Relations Area.
    18. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    19. Roberto Morcillo-Jimenez & Karel Gutiérrez-Batista & Juan Gómez-Romero, 2023. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data," Energies, MDPI, vol. 16(4), pages 1-29, February.
    20. Zech, Matthias & von Bremen, Lueder, 2024. "End-to-end learning of representative PV capacity factors from aggregated PV feed-ins," Applied Energy, Elsevier, vol. 361(C).

    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:s0306261922017998. 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.