@article{LIU2026, 
author = {Jie LIU and Bowen ZHOU and Ming TIAN and Ke HAN},
title = {Short-term load forecasting based on transfer learning and TCN-BiGRU},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {4},
pages = {995-1004},
keywords = {short-term load forecasting, transfer learning, temporal convolutional network, K-medoids clustering, fusion network},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2024.0056},
doi = {10.13700/j.bh.1001-5965.2024.0056},
abstract = {Electricity load forecasting is of great significance to the stable operation of power systems. For load forecasting, traditional short-term forecasting techniques frequently employ linear regression models, which have low forecasting accuracy due to the models’ inability to incorporate complicated load changes. A temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU) model based on transfer learning (TL) is proposed. Highly relevant information is moved into the experimental model using a transfer learning strategy; the data is clustered and analyzed using a K-medoids clustering algorithm; features at various TCN scales are extracted using a parallel convolution strategy; pertinent information is captured using temporal attention (TA); and the TCN training is further extracted using a BiGRU. The non-linear features of the output are further extracted using the dynamic multigroup particle swarm optimization (DMS-PSO) algorithm to optimize and tune the hyperparameters of the network training in order to find the best combination of hyperparameters. The experimental results show that the proposed TL-TCN-BiGRU algorithm reduces mean absolute error (MAE) by 38.6%, root mean square error (RMSE) by 40.7%, mean absolute percentage error (MAPE) by 30.4%, and R2 by 5.3% relative to the gated recurrent unit (GRU).}
}