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This study aims to enhance the accuracy of wind power prediction using an improved algorithm.

Application research of wind power prediction method based on improved seagull algorithm

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Optimized wind power prediction model demonstrates high accuracy in various wind scenarios, ensuring grid stability.

This study aims to enhance the accuracy of wind power prediction using an improved algorithm.
Developed an improved seagull optimization algorithm (LSC-SOA) using logistic-sine-cosine mapping.
Combined LSC-SOA with a backpropagation neural network (BP) to create the LSC-SOA-BP prediction model.
Evaluated model performance with metrics like RMSE, MAE, and R2 on test data.
Achieved an RMSE of 0.11 and MAE of 0.08 on the test set.
The model showed an R2 score of 0.97, indicating a strong correlation between predicted and actual outputs.
Performance was at least 30.3% better than comparison models under varying wind speed conditions.

Wu et al. (2026) studied this question.

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View Full Paper

Ask AI

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Authors

Optimized wind power prediction model demonstrates high accuracy in various wind scenarios, ensuring grid stability.

This study aims to enhance the accuracy of wind power prediction using an improved algorithm.
Developed an improved seagull optimization algorithm (LSC-SOA) using logistic-sine-cosine mapping.
Combined LSC-SOA with a backpropagation neural network (BP) to create the LSC-SOA-BP prediction model.
Evaluated model performance with metrics like RMSE, MAE, and R2 on test data.
Achieved an RMSE of 0.11 and MAE of 0.08 on the test set.
The model showed an R2 score of 0.97, indicating a strong correlation between predicted and actual outputs.
Performance was at least 30.3% better than comparison models under varying wind speed conditions.

Wu et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark