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The aim is to enhance the prediction accuracy of secondary network supply water temperature for better energy utilization.

July 2, 2026Scientific ReportsOpen Access

Prediction of secondary network supply water temperature based on EEMD-DWA-CNN-BiLSTM model for energy conservation

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The aim is to enhance the prediction accuracy of secondary network supply water temperature for better energy utilization.
Utilized Ensemble Empirical Mode Decomposition (EEMD) for multi-scale time series decomposition.
Employed Convolutional Neural Network (CNN) for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for temporal modeling.
Constructed a predictive framework integrating EEMD, DWA, CNN, and BiLSTM using data from a heat exchange station.
Achieved a Mean Absolute Error (MAE) of 0.31, indicating high prediction accuracy.
Mean Absolute Percentage Error (MAPE) was 0.68, reflecting reliable performance.
Coefficient of determination (R²) reached 88.54, underscoring the model's effectiveness.

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Randomized trial evaluates accurate water temperature prediction in heating systems, suggesting improved energy efficiency.

Wang et al. (2026) studied this question.

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