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Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model

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  • Jiang, Feifeng
  • Ma, Jun
  • Li, Zheng
  • Ding, Yuexiong

Abstract

Prediction of building energy performance is a critical strategy for building energy management. Extant studies established city-scale prediction models only based on buildings with energy data. However, building energy data in most cities is limited, which may impair model performance. A large number of unlabeled buildings (without energy data) may reveal important energy use knowledge, but few studies have explored their capability to improve building energy prediction. Therefore, a novel semi-supervised deep learning method, namely dynamically updated multi-fold semi-supervised learning method based on deep neural networks (DUMSL-DNN) is proposed to predict building energy use intensity (EUI) by utilizing unlabeled samples. Manhattan is selected as a case study, which contains 4854 labeled samples and 34,456 unlabeled samples. Compared with the optimal DNN model, DUMSL-DNN can improve building EUI prediction with root-mean-square error (RMSE) reduced by 9.36% and mean absolute error (MAE) reduced by 9.43%. The DUMSL method is superior to typical semi-supervised learning methods with the lowest RMSE of 0.5207 and the lowest MAE of 0.3325. By the implementation of DUMSL-DNN, more areas with high EUI are identified in Manhattan. Specifically, commercial buildings and residential buildings built before 1965 have higher EUI. Measures are accordingly proposed to improve building energy efficiency.

Suggested Citation

  • Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222005345
    DOI: 10.1016/j.energy.2022.123631
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    as
    1. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    2. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Park, Junyoung & Park, Jinkyoo, 2019. "Physics-induced graph neural network: An application to wind-farm power estimation," Energy, Elsevier, vol. 187(C).
    4. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    5. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    6. Wang, Wei & Hong, Tianzhen & Li, Nan & Wang, Ryan Qi & Chen, Jiayu, 2019. "Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification," Applied Energy, Elsevier, vol. 236(C), pages 55-69.
    7. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    8. Wang, Zhenyu & Song, Chunfeng & Chen, Tao, 2017. "Deep learning based monitoring of furnace combustion state and measurement of heat release rate," Energy, Elsevier, vol. 131(C), pages 106-112.
    9. Ma, Jun & Cheng, Jack C.P. & Jiang, Feifeng & Chen, Weiwei & Zhang, Jingcheng, 2020. "Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques," Land Use Policy, Elsevier, vol. 94(C).
    10. Guo, Siyue & Yan, Da & Hu, Shan & Zhang, Yang, 2021. "Modelling building energy consumption in China under different future scenarios," Energy, Elsevier, vol. 214(C).
    11. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    12. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    13. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    14. 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).
    15. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    16. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    17. Fuchs, Marcus & Teichmann, Jens & Lauster, Moritz & Remmen, Peter & Streblow, Rita & Müller, Dirk, 2016. "Workflow automation for combined modeling of buildings and district energy systems," Energy, Elsevier, vol. 117(P2), pages 478-484.
    18. Chou, Jui-Sheng & Truong, Dinh-Nhat & Kuo, Ching-Chiun, 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, Elsevier, vol. 224(C).
    19. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    20. 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.
    21. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).
    22. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    23. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    24. Chung, William & Hui, Y.V. & Lam, Y. Miu, 2006. "Benchmarking the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 83(1), pages 1-14, January.
    25. 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.
    26. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    27. Tang, Hong & Wang, Shengwei, 2021. "Energy flexibility quantification of grid-responsive buildings: Energy flexibility index and assessment of their effectiveness for applications," Energy, Elsevier, vol. 221(C).
    28. 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.
    29. Zhao, Jing & Xin, Yajuan & Tong, Dingding, 2012. "Energy consumption quota of public buildings based on statistical analysis," Energy Policy, Elsevier, vol. 43(C), pages 362-370.
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    6. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).

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