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District heating load prediction algorithm based on bidirectional long short-term memory network model

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  • Cui, Mianshan

Abstract

Heating load prediction based on machine learning algorithms has received increasing attention, especially the Long Short Term Memory (LSTM) network, have been shown to have a superior performance in predicting the heat load consumption. However, most of the current research reports on load prediction models using LSTM models are focused on the unidirectional (Uni-LSTM) network. In this paper, a bidirectional (Bi-LSTM) network for heat load prediction is proposed to make full use of the model hyperparameters to obtain the optimal model and to fully compare with the Uni-LSTM model, and the Bi-LSTM model can improve the prediction accuracy of heat load in a district heating system by using both past and future weather information. In addition, the two types of models are set up with different depth-stacked layers, and for each of the proposed models, a hyperparametric optimization tool has been used to obtain the best model. The results indicate that the increase in depth-stacked LSTM layers has no significant improvement in the prediction accuracy. The input time series length reflects the inertia influence duration of the district heating system, and the optimal model can be obtained for different settings of input time series length. The best optimally configured models were compared, and the single-layer Bi-LSTM model outperformed the single-layer Uni-LSTM model by 19.56%, 16.43%, 14.16%, and 20.69% in terms of the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE), Mean Absolute Error (MAE), and the coefficient of variation of the RMSE (CV-RMSE), respectively.

Suggested Citation

  • Cui, Mianshan, 2022. "District heating load prediction algorithm based on bidirectional long short-term memory network model," Energy, Elsevier, vol. 254(PA).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pa:s0360544222011860
    DOI: 10.1016/j.energy.2022.124283
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    Cited by:

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    2. Wang, Zhijin & Liu, Xiufeng & Huang, Yaohui & Zhang, Peisong & Fu, Yonggang, 2023. "A multivariate time series graph neural network for district heat load forecasting," Energy, Elsevier, vol. 278(PA).
    3. Dong, Zhe & Cheng, Zhonghua & Zhu, Yunlong & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2024. "Passivity-based control of fluid flow networks with capacitance," Energy, Elsevier, vol. 299(C).
    4. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "District heating load patterns and short-term forecasting for buildings and city level," Energy, Elsevier, vol. 289(C).
    5. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
    6. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
    7. Shi, Jian & Teh, Jiashen, 2024. "Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion," Applied Energy, Elsevier, vol. 353(PB).

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