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Optimized Seq2Seq model based on multiple methods for short-term power load forecasting
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DOI：10.1016/j.asoc.2023.110335.png)
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Abstract
En 中文
Accurate power load prediction plays a key role in reducing resource waste 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.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Power load forecasting
Convolutional neural network
Attention mechanism
Sequence to Sequence
Bidirectional long-short term memory
network
Bayesian optimization
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6.6
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1.4W
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4.8W
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