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| Short-term power load forecasting method based on improved hierarchical transfer learning andmulti-scale CNN-BiLSTM-Attention |
| DOI：10.19783/j.cnki.pspc.220422 |
| Key Words:power load forecasting CNN BiLSTM attention mechanism transfer learning |
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| Abstract:Insufficient power load data samples in the target domain result in inadequate model training and low prediction accuracy. Thus an improved hierarchical transfer learning strategy combined with a multi-scale CNN-BiLSTM-Attention model is proposed for short-term power load forecasting. A multi-scale CNN superimposed, linked and in parallel is designed as a feature extractor, and the features are then passed as input to two BiLSTM structures for further learning. Then an attention mechanism is introduced to adjust the weight of the captured information vector. This paper divides the layers according to the structure of the basic model, and inputs the source data into the model according to the level of the goodness-of-fit value to perform hierarchical transfer learning training. It then retains the optimal training weight of each layer, and uses the target domain data to carry out model training and obtain the final predictive model after fine-tuning. Experiments show that the proposed multi-scale CNN-BiLSTM-Attention model can effectively improve the accuracy of load prediction. When the load data samples are insufficient, the improved hierarchical transfer learning strategy can effectively reduce the prediction error compared with traditional transfer learning. Taking six months of data in the target domain as an example, compared with traditional transfer learning, the MAPE is reduced by 13.31%, MAE is reduced by 15.16%, and RMSE is reduced by 14.37%.
This work is supported by the Basic Public Welfare Research Program of Zhejiang Province (No. LGG22E070003). |
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