Heat Load Forecasting Model Based on AWOA-TEFN
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
| Abstract: | Against the strategic backdrop of energy conservation and emission reduction, the intelligent optimization of regional heating systems has become a key link in improving energy utilization efficiency and reducing carbon emissions, with accurate heat load forecasting serving as the core technological support to achieve this goal. To address the complex characteristics of heat load sequences, such as nonlinearity, pronounced non-stationarity, and the coupling of multiple influencing factors, and to overcome the shortcomings of existing forecasting models—namely insufficient capture of uncertainty features, hyperparameter dependence on manual tuning, and inadequate integration of multivariate time series information—this paper proposes a heat load forecasting scheme based on an Adaptive Whale Optimization Algorithm-optimized Time Evidence Fusion Network (AWOA-TEFN). This scheme centers on evidence theory and information fusion technology, and, through three key modules—basic probability assignment, expectation fusion, and time normalization-denormalization—it accurately captures the uncertainty characteristics of multivariate time series from both temporal and channel dimensions. During the data preprocessing stage, cubic spline interpolation is used to fill in missing values, the quartile method is employed to remove outliers, and core input features are selected based on Pearson correlation coefficients. Additionally, the AWOA algorithm adaptively optimizes critical hyperparameters, such as input sequence length and prediction step size, significantly enhancing model robustness and adaptability. Based on hourly measured heat load data from the Anyang heat exchange station, the proposed scheme is compared with mainstream forecasting algorithms, including GRU, RFR, LSTM, and LightGBM. Experimental results indicate that the AWOA-TEFN scheme outperforms the comparative algorithms across all evaluation metrics, including mean absolute percentage error, accurately tracking the dynamic trends of heat load changes. This provides reliable technical support for the optimal configuration, intelligent regulation, and energy efficiency improvement of heating systems. |
| Keywords: | Heat Load Forecasting; Adaptive Whale Optimization Algorithm (AWOA); Temporal Evidence Fusion Network (TEFN); Deep Learning; Data Preprocessing |
| DOI: | 10.57237/j.jest.2025.04.001 |
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Received: 5 December 2025
Accepted: 24 December 2025
Published Online: 29 December 2025
Funding
| 1. | 大学生创新训练计划项目 (202510069001) |
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