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Accurate power load prediction plays a key role in reducing resource wastes and ensuring stable and safe operations of power systems. To address the problems of poor stability and unsatisfactory prediction accuracy of existing prediction methods, in this paper, we propose a novel approach for short-term power load prediction by improving the sequence to sequence (Seq2Seq) model based on bidirectional long-short term memory (Bi-LSTM) network. Different from existing prediction models, we apply convolutional neural network, attention mechanism, and Bayesian optimization for the improvement of the Seq2Seq model. Moreover, in the data processing stage, we use the random forest algorithm for feature selection, and also adopt the weighted grey relational projection algorithm for holiday load processing to process the data and thereby overcome the difficulty of holiday load prediction. To validate our model, we choose the power load dataset in Singapore and Switzerland as experimental data and compare our prediction results with those by other models to show that our method can generate a higher prediction accuracy.
Bibliographical noteFunding Information:
This work is supported by the National Natural Science Foundation of China (No. 72171126 ), Ministry of Education Project of Humanities and Social Science (No. 20YJA630009 ). Natural Science Foundation of Shandong Province (No. ZR2022MG002 ). This work is also financially supported by the Faculty Researcrant (FRG) of Lingnan University (No. DB21B1 ).
© 2023 Elsevier B.V.
- Power load forecasting
- Convolutional neural network
- Attention mechanism
- Sequence to Sequence
- Bidirectional long-short term memory network
- Bayesian optimization
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- 1 Finished
1/06/21 → 31/05/23
Project: Grant Research