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Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism

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  • Zhang, Fan
  • Bales, Chris
  • Fleyeh, Hasan

Abstract

District heating systems that distribute heat through pipelines to residential and commercial buildings have been widely used in Northern Europe and according to the latest study (Werner, 2017) [1], district heating has the largest share of the heat supply market in Sweden. Therefore, energy efficiency of district heating systems is of great interest to energy stakeholders. However, it is common that district heating systems fail to achieve the expected performance due to various faults or inappropriate operations. Night setback is one control strategy that has been proved to be not a suitable setting for well insulated modern buildings in terms of both economic factor and energy efficiency. Especially, night setback leads to sudden morning peak that can be problematic to utility companies. In this study, a bidirectional long short term memory neural network based approach with attention mechanism is proposed for classifying substations that use night setback regularly. To evaluate the effectiveness of the proposed approach, data of 10 anonymous substations in Sweden are used in the case study. Precision, recall, and f1 score are used as the performance measures. Results of out of sample testing show that the proposed approach outperform the baseline models in this study.

Suggested Citation

  • Zhang, Fan & Bales, Chris & Fleyeh, Hasan, 2021. "Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism," Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221004126
    DOI: 10.1016/j.energy.2021.120163
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    References listed on IDEAS

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    1. Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
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    Cited by:

    1. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).

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