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An Integrated Prediction Model for Building Energy Consumption: A Case Study

In: Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Ting Hu

    (Water Resources Bureau of Xingyi
    Shenzhen University)

  • Zhikun Ding

    (Water Resources Bureau of Xingyi
    Shenzhen University)

Abstract

As a large energy consumer, the building sector accounts for 30–40% of energy consumption and around 40% of carbon emissions. How to improve energy efficiency in the building sector has become an urgent issue in urban sustainable development. Building energy prediction is a flexible and cost-efficient approach to improve energy efficiency. Green buildings can also improve energy efficiency but the energy saving is still lower than expected. Hence, is it is very important to improve the energy efficiency of green buildings. However, research on green building energy consumption prediction is not sufficient. To improve prediction accuracy, an integration model for energy consumption forecast was proposed. Data were collected from a green building for one year period in Shenzhen. Results showed that the proposed model had higher prediction accuracy, which validated the integration model. Meanwhile, the eight typical building operational patterns of energy consumption were identified according to the hour, month and day type. Model can be used to evaluate different design schemes and building operation strategies as well as real-time fault detection and diagnosis. The proposed model will improve the energy efficiency of green buildings; reduce building energy consumption and carbon emissions.

Suggested Citation

  • Ting Hu & Zhikun Ding, 2021. "An Integrated Prediction Model for Building Energy Consumption: A Case Study," Springer Books, in: Gui Ye & Hongping Yuan & Jian Zuo (ed.), Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, pages 1655-1665, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-8892-1_116
    DOI: 10.1007/978-981-15-8892-1_116
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    Citations

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    Cited by:

    1. Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
    2. Seppo Sierla & Heikki Ihasalo & Valeriy Vyatkin, 2022. "A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems," Energies, MDPI, vol. 15(10), pages 1-25, May.
    3. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
    4. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.

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