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Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition

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  • Xiao, Tong
  • Xu, Peng
  • He, Ruikai
  • Sha, Huajing

Abstract

With the evolution of new energy and carbon trading systems, it is important to accurately predict building energy consumption to help energy arrangements. Additionally, the widespread use of smart meters has introduced a new data context for building energy prediction. Building energy prediction techniques need improvement but the ideas of various new prediction methods are still on the way and have not yet been compared and tested side-by-side in the reported studies. Thus, we held a competition called ‘Energy Detective’. To investigate the status quo of the current prediction techniques, we designed a representative prediction case: cross-building prediction with limited physical parameters and historical data. A total of 195 participants formed 89 teams to participate in the competition. This paper describes the models presented in the competition. By analysing the methods and results, we identified strategies for the future development of energy prediction in hybrid modelling and data-driven modelling. For hybrid modelling, we discuss the basic strategies for hybrid models and suggest that more hybrid models can be developed by combining a wide variety of individual models in sequence or parallel or via feedback methods to achieve accurate and interpretable models. For data-driven modelling, we analyse and discuss the areas of improvement for the current data-driven workflow and suggest that processes other than model application are also important and should be carefully considered. Considering the increasing amount of data available for prediction, we discuss the shortcomings and suggestions for improving the current data preparation process. We recommend comprehensive consideration of the anomaly types in data pre-processing and a focus on feature engineering for higher accuracy and model interpretability, while emphasising the vital role of data selection in cross-building energy prediction.

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  • Xiao, Tong & Xu, Peng & He, Ruikai & Sha, Huajing, 2022. "Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921011570
    DOI: 10.1016/j.apenergy.2021.117829
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    as
    1. Hong, Tao & Xie, Jingrui & Black, Jonathan, 2019. "Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1389-1399.
    2. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    3. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    4. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    5. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    6. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    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. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    9. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    10. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    11. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
    12. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
    13. Pan, Wei & Garmston, Helen, 2012. "Compliance with building energy regulations for new-build dwellings," Energy, Elsevier, vol. 48(1), pages 11-22.
    14. 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.
    15. Miller, Clayton & Nagy, Zoltán & Schlueter, Arno, 2018. "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1365-1377.
    Full references (including those not matched with items on IDEAS)

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    4. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
    5. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
    6. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
    7. 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).
    8. Yang, Xining & Hu, Mingming & Tukker, Arnold & Zhang, Chunbo & Huo, Tengfei & Steubing, Bernhard, 2022. "A bottom-up dynamic building stock model for residential energy transition: A case study for the Netherlands," Applied Energy, Elsevier, vol. 306(PA).

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