Short Term Wind Power Forecasting Based on Improved Dung Beetle Optimization Algorithm
[1]JIANG Jiandong,ZHANG HaifenfGUO jiaqi.Short Term Wind Power Forecasting Based on Improved Dung Beetle Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre):2-.[doi:10. 13705 / j. issn. 1671-6833. 2025. 01. 015]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
45
Number of periods:
2024 pre
Page number:
2-
Column:
Public date:
2024-12-31
- Title:
- Short Term Wind Power Forecasting Based on Improved Dung Beetle Optimization Algorithm
- Keywords:
- wind power forecasting; improved dung beetle optimization algorithm; variational mode decomposition (VMD); convolutional neural networks (CNN); bidirectional long short term memory neural network ( BiLSTM)
- CLC:
- TM614
- DOI:
- 10. 13705 / j. issn. 1671-6833. 2025. 01. 015
- Abstract:
- A short-term wind power prediction model based on POTDBO-VMD-CNN- BiLSTM is proposed in the thesis to improve the accuracy of short-term wind power prediction. Firstly, three strategies are adopted to improve the dung beetle optimization algorithm, including integrating Piecewise chaotic mapping, integrating Osprey optimization algorithm, and integrating adaptive T-distribution perturbation, in order to balance the global exploration and local development capabilities of the dung beetle optimization algorithm and accelerate its convergence speed . Secondly, the improved Dung Beetle Optimization algorithm ( POTDBO) is used to optimize the decomposition number and penalty factor of Variational Mode Decomposition (VMD) to improve the decomposition effect of VMD. Then, the POTDBO-VMD model is used to decompose the wind power . Finally, the decomposed frequency components and residual components are input into the CNN-BiLSTM hybrid model for prediction, and the prediction results of each frequency component and residual component are sequentially reconstructed to obtain the wind power prediction results. The proposed model is experimentally tested using actual data from wind farm s in Xinjiang and Jilin . Compared with the CNN-BiLSTM model , the results show that the model in this thesis increases by 4.21% and 7.14% on R 2 respectively, demonstrating better prediction accuracy demonstrates better prediction accuracy
- References:
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Last Update:
2024-10-10