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Research on thermal load prediction of district heating station based on transfer learning

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  • Wang, Chendong
  • Yuan, Jianjuan
  • Huang, Ke
  • Zhang, Ji
  • Zheng, Lihong
  • Zhou, Zhihua
  • Zhang, Yufeng

Abstract

Precise prediction of thermal load plays a critical role in China to fulfill the demand of energy saving, carbon emission reduction and environmental protection, and to realize the "3060″ target. This study proposed layer transfer model and merged transfer model for thermal load prediction of the district heating station. Experiment schemes were elaborated to simulate cross-year and cross-site scenarios, and practical data was collected serving the experiments. The prediction accuracy can be maintained without degradation in cross-year scenario, specifically, the coefficient of variation of the root mean squared error fluctuated between −1.09% and +0.45% compared to previous heating season when proposed two models were used. In the cross-site scenario, proposed models can achieve good prediction performance when the training data is insufficient. The coefficient of variation of the root mean squared error of the new model with insufficient training data was reduced by 7.62% on average when merged transfer model was used, which is equivalent to an overall reduction of 41.67%. Furthermore, proposed models can be applied to further optimize the prediction performance, even if beyond the scenarios discussed in this study.

Suggested Citation

  • Wang, Chendong & Yuan, Jianjuan & Huang, Ke & Zhang, Ji & Zheng, Lihong & Zhou, Zhihua & Zhang, Yufeng, 2022. "Research on thermal load prediction of district heating station based on transfer learning," Energy, Elsevier, vol. 239(PE).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221025573
    DOI: 10.1016/j.energy.2021.122309
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    References listed on IDEAS

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    1. Lu, Yakai & Tian, Zhe & Zhang, Qiang & Zhou, Ruoyu & Chu, Chengshan, 2021. "Data augmentation strategy for short-term heating load prediction model of residential building," Energy, Elsevier, vol. 235(C).
    2. Wang, Chendong & Yuan, Jianjuan & Zhang, Ji & Deng, Na & Zhou, Zhihua & Gao, Feng, 2020. "Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing," Energy, Elsevier, vol. 202(C).
    3. 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.
    4. Ma, Minda & Ma, Xin & Cai, Wei & Cai, Weiguang, 2020. "Low carbon roadmap of residential building sector in China: Historical mitigation and prospective peak," Applied Energy, Elsevier, vol. 273(C).
    5. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    6. Yuan, Jianjuan & Zhou, Zhihua & Tang, Huajie & Wang, Chendong & Lu, Shilei & Han, Zhao & Zhang, Ji & Sheng, Ying, 2020. "Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system," Energy, Elsevier, vol. 199(C).
    7. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    8. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
    9. 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).
    10. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei & Zhou, Zhihua, 2022. "Evaluation of the operation data for improving the prediction accuracy of heating parameters in heating substation," Energy, Elsevier, vol. 238(PB).
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    3. Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).
    4. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).

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