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ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement

Author

Listed:
  • Xin Zhou

    (School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

  • Xin Meng

    (School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

  • Zhenyu Li

    (School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

Abstract

To reduce the energy consumption of domestic hot water (DHW) production, it is necessary to reasonably select a water supply plan through early predictions of DHW consumption to optimize energy consumption. However, the fluctuations and intermittence of DHW consumption bring great challenges to the prediction of water consumption. In this paper, an ANN-LSTM-A water quantity prediction model based on attention mechanism (AM) enhancement is improved. The model includes an input layer, an AM layer, a hidden layer, and an output layer. Based on the combination of artificial neural network (ANN) and long short-term memory (LSTM) models, an AM is incorporated to address the issue of the traditional ANN model having difficulty capturing the long-term dependencies, such as lags and trends in time series, to improve the accuracy of the DHW consumption prediction. Through comparative experiments, it was found that the root mean square error of the ANN-LSTM-A model was 15.4%, 13.2%, and 13.2% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The corresponding mean absolute error was 17.9%, 11.5%, and 8% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The results showed that the proposed ANN-LSTM-A model yielded better performances in predicting DHW consumption than the ANN, LSTM, and ANN-LSTM models. This work provides an effective reference for the reasonable selection of the water supply plan and optimization of energy consumption.

Suggested Citation

  • Xin Zhou & Xin Meng & Zhenyu Li, 2024. "ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement," Energies, MDPI, vol. 17(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1102-:d:1345730
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    References listed on IDEAS

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    1. Lyu, Yizheng & Gao, Hanbo & Yan, Kun & Liu, Yingjie & Tian, Jinping & Chen, Lyujun & Wan, Mei, 2022. "Carbon peaking strategies for industrial parks: Model development and applications in China," Applied Energy, Elsevier, vol. 322(C).
    2. Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
    3. Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
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