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Predicting the electric power consumption of office buildings based on dynamic and static hybrid data analysis

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  • Zou, Rongwei
  • Yang, Qiliang
  • Xing, Jianchun
  • Zhou, Qizhen
  • Xie, Liqiang
  • Chen, Wenjie

Abstract

Currently, building energy consumption accounts for a considerable proportion of the world's energy consumption, e.g., 35 % in China, which has become a key concern with its rising proportion. Accurate prediction of building hourly energy consumption is key to realizing green, low-carbon, and energy-saving modern buildings. However, most of the current prediction methods are only based on dynamic data from IoT (Internet of Things) systems that may contain a large number of outliers, resulting in lower accuracy and reliability. To solve this problem, with the help of rich data provided by BIM (Building Information Modelling), we propose an approach named dynamic and static hybrid data analysis (D&S-HDA) that can provide a solution with better prediction accuracy. Technically, the novelties of D&S-HDA are fourfold. Firstly, we establish the D&S-HDA framework to obtain more accurate prediction outcomes, where the electricity consumption prediction results based on dynamic data analysis are weighted with the estimation values based on static data analysis. Secondly, in the dimension of dynamic data analysis, we design an improved temporal convolutional networks (TCNs) for parallelly outputting dynamic data samples to make the prediction results more intuitive. Thirdly, in the dimension of static data analysis, a novel concept of building hourly power consumption coefficient(BHPCC) is proposed, and its calculation method using static data analysis is designed to estimate hourly electricity consumption. Finally, in order to validate the effectiveness of our D&S-HDA approach, we design multidimensional evaluation metrics and conduct multi-group comparative experiments under different conditions, including climate models, electricity usage behaviour and time scales. The experimental results reveal that the proposed D&S-HDA approach outperforms current mainstream works in terms of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), with values of 1.5248, 1.0693 and 2.9505, respectively, which shows the efficiency and feasibility of the proposed D&S-HDA.

Suggested Citation

  • Zou, Rongwei & Yang, Qiliang & Xing, Jianchun & Zhou, Qizhen & Xie, Liqiang & Chen, Wenjie, 2024. "Predicting the electric power consumption of office buildings based on dynamic and static hybrid data analysis," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035430
    DOI: 10.1016/j.energy.2023.130149
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    References listed on IDEAS

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    1. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    2. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    3. 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.
    4. 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.
    5. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    6. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    7. 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.
    8. 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.
    9. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    10. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    11. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
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