Short-term Prediction of Wind and Photovoltaic Power Based on the Fusion of Attention Mechanism and Improved Whale Optimization Algorithm

With the aim of enhancing the accuracy of predictions and stability of wind and solar power generation prediction models in different scenarios, a deep learning hybrid prediction model integrating the attention mechanism and the improved whale optimization algorithm is proposed. Firstly, Pearson cor...

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Vydáno v:	Journal of physics. Conference series Ročník 3135; číslo 1; s. 12021 - 12026
Hlavní autoři:	Zou, Jin, Ye, Jiaqing, Xue, Zhiliang, Zhang, Li, Wu, Feiyun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bristol IOP Publishing 01.11.2025
Témata:	Accuracy Algorithms Correlation analysis Improve the whale optimization algorithm Long short-term memory Attention mechanism Luminous intensity Optimization Optimization algorithms Pearson correlation Photovoltaic and wind power short-term forecasting Prediction models Solar energy Solar power generation Wind speed
ISSN:	1742-6588, 1742-6596, 1742-6596
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Abstract	With the aim of enhancing the accuracy of predictions and stability of wind and solar power generation prediction models in different scenarios, a deep learning hybrid prediction model integrating the attention mechanism and the improved whale optimization algorithm is proposed. Firstly, Pearson correlation analysis is employed to measure the association between each feature and wind and solar power in order to select the meteorological features with stronger correlation. Secondly, given the limitations of traditional long short-term memory (LSTM) and Gate Recurrent Unit (GRU) models in power prediction, the LSTM-GRU hybrid prediction model is adopted for accurate short-term power prediction of wind and solar power. Then, a hybrid prediction model integrating the Attention mechanism (Attention) and the improved whale algorithm (IWOA) is put forward to enhance accuracy of wind and solar energy forecasts; Finally, the historical data of a certain new energy base in northwest China was taken as the experimental data, and the Attention-IWOA-LSTM-GRU model was used for prediction. The outcomes of the simulation indicate that, compared with the prediction effects of other models in the circumstances involving abrupt fluctuations in wind velocity and light intensity, the prediction accuracy of the Attention-IWOA-LSTM-GRU model is higher.
AbstractList	With the aim of enhancing the accuracy of predictions and stability of wind and solar power generation prediction models in different scenarios, a deep learning hybrid prediction model integrating the attention mechanism and the improved whale optimization algorithm is proposed. Firstly, Pearson correlation analysis is employed to measure the association between each feature and wind and solar power in order to select the meteorological features with stronger correlation. Secondly, given the limitations of traditional long short-term memory (LSTM) and Gate Recurrent Unit (GRU) models in power prediction, the LSTM-GRU hybrid prediction model is adopted for accurate short-term power prediction of wind and solar power. Then, a hybrid prediction model integrating the Attention mechanism (Attention) and the improved whale algorithm (IWOA) is put forward to enhance accuracy of wind and solar energy forecasts; Finally, the historical data of a certain new energy base in northwest China was taken as the experimental data, and the Attention-IWOA-LSTM-GRU model was used for prediction. The outcomes of the simulation indicate that, compared with the prediction effects of other models in the circumstances involving abrupt fluctuations in wind velocity and light intensity, the prediction accuracy of the Attention-IWOA-LSTM-GRU model is higher.
Author	Wu, Feiyun Ye, Jiaqing Xue, Zhiliang Zou, Jin Zhang, Li
Author_xml	– sequence: 1 givenname: Jin surname: Zou fullname: Zou, Jin organization: State Grid Ningbo Electric Power Supply Company Ningbo, China – sequence: 2 givenname: Jiaqing surname: Ye fullname: Ye, Jiaqing organization: State Grid Ningbo Electric Power Supply Company Ningbo, China – sequence: 3 givenname: Zhiliang surname: Xue fullname: Xue, Zhiliang organization: State Grid Ningbo Electric Power Supply Company Ningbo, China – sequence: 4 givenname: Li surname: Zhang fullname: Zhang, Li organization: State Grid Ningbo Electric Power Supply Company Ningbo, China – sequence: 5 givenname: Feiyun surname: Wu fullname: Wu, Feiyun organization: China Three Gorges University College of Electrical Engineering and New Energy, Yichang 443002, China
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Cites_doi	10.1016/j.asoc.2025.113345 10.1016/j.jclepro.2021.126564
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SubjectTerms	Accuracy Algorithms Correlation analysis Improve the whale optimization algorithm Long short-term memory Attention mechanism Luminous intensity Optimization Optimization algorithms Pearson correlation Photovoltaic and wind power short-term forecasting Prediction models Solar energy Solar power generation Wind speed
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Title	Short-term Prediction of Wind and Photovoltaic Power Based on the Fusion of Attention Mechanism and Improved Whale Optimization Algorithm
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