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Assessment of deep recurrent neural network-based strategies for short-term building energy predictions

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  • Fan, Cheng
  • Wang, Jiayuan
  • Gang, Wenjie
  • Li, Shenghan

Abstract

Accurate and reliable building energy predictions can bring significant benefits for energy conservations. With the development in smart buildings, massive amounts of building operational data are being collected and available for analysis. It is desired to develop big data-driven methods to fully realize the potential of building operational data in energy predictions. This paper investigates the usefulness of advanced recurrent neural network-based strategies for building energy predictions. Each strategy presents unique characteristics at two levels. At the high level, three inference approaches are used for generating short-term predictions, including the recursive approach, the direct approach and the multi-input and multi-output (MIMO) approach. At the low level, the state-of-the-art techniques are utilized for recurrent model development, such as the use of one-dimensional convolutional operations, bidirectional operations, and different types of recurrent units. The performance of different strategies has been assessed from different perspectives based on real building operational data. The research results help to bridge the knowledge gap between building professionals and advanced big data analytics. The insights obtained can be used as guidelines and references for developing advanced deep recurrent models for short-term building energy predictions.

Suggested Citation

  • Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
    • Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:700-710
    DOI: 10.1016/j.apenergy.2018.12.004
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    References listed on IDEAS

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    1. 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.
    2. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    3. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    4. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    5. An, Jingjing & Yan, Da & Hong, Tianzhen & Sun, Kaiyu, 2017. "A novel stochastic modeling method to simulate cooling loads in residential districts," Applied Energy, Elsevier, vol. 206(C), pages 134-149.
    6. Salcedo-Sanz, S. & Cornejo-Bueno, L. & Prieto, L. & Paredes, D. & García-Herrera, R., 2018. "Feature selection in machine learning prediction systems for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 728-741.
    7. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    8. Klein, Konstantin & Herkel, Sebastian & Henning, Hans-Martin & Felsmann, Clemens, 2017. "Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options," Applied Energy, Elsevier, vol. 203(C), pages 917-937.
    9. Gao, Dian-ce & Wang, Shengwei & Shan, Kui & Yan, Chengchu, 2016. "A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems," Applied Energy, Elsevier, vol. 164(C), pages 1028-1038.
    10. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
    11. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
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