Wind Power Prediction Model based on Integrated Osprey and Adaptive T-distribution Dung Beetle Optimization Algorithm
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Energy Load and Power Forecasting · Power Systems and Renewable Energy · Evaluation Methods in Various Fields
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (41)
Show source-provided references
1. Fang, P., Fu, W., Wang, K., Xiong, D., & Zhang, K. (2022). A compositive architecture coupling outlier correction, ewt, nonlinear volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting. Applied Energy,307, 118191. https://doi.org/10.1016/j.apenergy.2021.11819110.1016/j.apenergy.2021.118191 ↗
2. Suo, L., Peng, T., Song, S., Zhang, C., Wang, Y., Fu, Y., & Nazir, M. S. (2023). Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm. Energy,276, 127526. https://doi.org/10.1016/j.energy.2023.12752610.1016/j.energy.2023.127526 ↗
3. Hossain, M. A., Chakrabortty, R. K., Elsawah, S., & Ryan, M. J. (2021). Very short-term forecasting of wind power generation using hybrid deep learning model. Journal of Cleaner Production,296, 126564. https://doi.org/10.1016/j.jclepro.2021.12656410.1016/j.jclepro.2021.126564 ↗
4. Wan, C., Xu, Z., Pinson, P., Dong, Z. Y., & Wong, K. P. (2014). Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Transactions on Power Systems,29(3), 1033–1044. https://doi.org/10.1109/TPWRS.2013.228787110.1109/TPWRS.2013.2287871 ↗
5. Wang, J., & Hu, J. (2015). A robust combination approach for short-term wind speed forecasting and analysis - combination of the arima (autoregressive integrated moving average), elm (extreme learning machine), svm (support vector machine) and lssvm (least square svm) forecasts using a gpr (gaussian process regression) model. Energy,93, 41–56. https://doi.org/10.1016/j.energy.2015.11.01610.1016/j.energy.2015.11.016 ↗
6. Vassallo, D., Krishnamurthy, R., Sherman, T., & Fernando, H. J. (2020). Analysis of random forest modeling strategies for multi-step wind speed forecasting. Energies,13(21), 5488. https://doi.org/10.3390/en1320548810.3390/en13205488 ↗
7. Singh, S., & Mohapatra, A. (2019). Repeated wavelet transform based arima model for very short-term wind speed forecasting. Renewable Energy,136, 758–768. https://doi.org/10.1016/j.renene.2019.01.03110.1016/j.renene.2019.01.031 ↗
8. Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. (2017). Recent advances in recurrent neural networks. arXiv preprint arXiv:1801.01078
9. Ju, Y., Sun, G., Chen, Q., Zhang, M., Zhu, H., & Rehman, M. U. (2019). A model combining convolutional neural network and light GBM algorithm for ultra-short-term wind power forecasting. IEEE Access,7, 28309–28318. https://doi.org/10.1109/ACCESS.2019.290271410.1109/ACCESS.2019.2902714 ↗
10. Shahid, F., Zameer, A., Mehmood, A., & Raja, M. A. Z. (2020). A novel wavenets long short term memory paradigm for wind power prediction. Applied Energy,269, 115098. https://doi.org/10.1016/j.apenergy.2020.11509810.1016/j.apenergy.2020.115098 ↗
11. Xiong, B., Lou, L., Meng, X., Wang, X., Ma, H., & Wang, Z. (2022). Short-term wind power forecasting based on attention mechanism and deep learning. Electric Power Systems Research,206, 107776. https://doi.org/10.1016/j.epsr.2022.10777610.1016/j.epsr.2022.107776 ↗
12. Belletreche, M., Bailek, N., Abotaleb, M., Bouchouicha, K., Zerouali, B., Guermoui, M., Kuriqi, A., Alharbi, A. H., Khafaga, D. S., & El-Shimy, M. (2024). Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions. Scientific Reports,14, 21842. https://doi.org/10.1038/s41598-024-73076-610.1038/s41598-024-73076-6 ↗
13. Xue, J., & Shen, B. (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing,79(7), 7305–7336. https://doi.org/10.1007/s11227-022-04959-610.1007/s11227-022-04959-6 ↗
14. Li, K., Huang, H., Fu, S., Ma, C., Fan, Q., & Zhu, Y. (2023). A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems. Computer Methods in Applied Mechanics and Engineering,415, 116199. https://doi.org/10.1016/j.cma.2023.11619910.1016/j.cma.2023.116199 ↗
15. Chakraborty, S., Saha, A. K., Chakraborty, R., Saha, M., & Nama, S. (2022). Hswoa: An ensemble of hunger games search and whale optimization algorithm for global optimization. International Journal of Intelligent Systems,37(1), 52–104. https://doi.org/10.1002/int.2261710.1002/int.22617 ↗
16. Tharwat, A., & Schenck, W. (2021). A conceptual and practical comparison of pso-style optimization algorithms. Expert Systems with Applications,167, 114430. https://doi.org/10.1016/j.eswa.2020.11443010.1016/j.eswa.2020.114430 ↗
17. Ali, A., Aadil, F., Khan, M. F., Maqsood, M., & Lim, S. (2023). Harris hawks optimization-based clustering algorithm for vehicular ad-hoc networks. IEEE Transactions on Intelligent Transportation Systems,24(6), 1–20. https://doi.org/10.1016/j.renene.2023.03.00110.1016/j.renene.2023.03.001 ↗
18. Duan, Y., & Yu, X. (2023). A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems. Expert Systems with Applications,213, 119017. https://doi.org/10.1016/j.eswa.2023.11901710.1016/j.eswa.2023.119017 ↗
19. Goh, H. H., He, R., Zhang, D., Liu, H., Dai, W., Lim, C. S., Kurniawan, T. A., Teo, K. T. K., & Goh, K. C. (2022). A multimodal approach to chaotic renewable energy prediction using meteorological and historical information. Applied Soft Computing,118, 108487. https://doi.org/10.1016/j.asoc.2022.10848710.1016/j.asoc.2022.108487 ↗
20. Zhang, S., Chen, Y., Xiao, J., Zhang, W., & Feng, R. (2021). Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism. Renewable Energy,174, 688–704. https://doi.org/10.1016/j.renene.2021.04.09110.1016/j.renene.2021.04.091 ↗
21. Wang, J., Zhang, L., Wang, C., & Liu, Z. (2021). A regional pretraining-classification-selection forecasting system for wind power point forecasting and interval forecasting. Applied Soft Computing,113, 107941. https://doi.org/10.1016/j.asoc.2021.10794110.1016/j.asoc.2021.107941 ↗
22. Lu, P., Ye, L., Pei, M., Zhao, Y., Dai, B., & Li, Z. (2022). Short-term wind power forecasting based on meteorological feature extraction and optimization strategy. Renewable Energy,184, 642–661. https://doi.org/10.1016/j.renene.2022.01.06610.1016/j.renene.2022.01.066 ↗
23. Wang, H., Li, G., Wang, G., Peng, J., Jiang, H., & Liu, Y. (2017). Deep learning based ensemble approach for probabilistic wind power forecasting. Applied Energy,188, 56–70. https://doi.org/10.1016/j.apenergy.2016.11.11110.1016/j.apenergy.2016.11.111 ↗
24. Ren, Y., Suganthan, P. N., & Srikanth, N. (2014). A novel empirical mode decomposition with support vector regression for wind speed forecasting. IEEE Transactions on Neural Networks and Learning Systems,27(8), 1793–1798. https://doi.org/10.1109/TNNLS.2014.235139110.1109/TNNLS.2014.2351391 ↗
25. Duan, J., Wang, P., Ma, W., Fang, S., & Hou, Z. (2022). A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting. International Journal of Electrical Power & Energy Systems,134, 107452. https://doi.org/10.1016/j.ijepes.2021.10745210.1016/j.ijepes.2021.107452 ↗
26. Yin, H., Ou, Z., Huang, S., & Meng, A. (2019). A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition. Energy,189, 116316. https://doi.org/10.1016/j.energy.2019.11631610.1016/j.energy.2019.116316 ↗
27. Zhang, W., Lin, Z., & Liu, X. (2022). Short-term offshore wind power forecasting-a hybrid model based on discrete wavelet transform (dwt), seasonal autoregressive integrated moving average (sarima), and deep-learning-based long short-term memory (lstm). Renewable Energy,185, 611–628. https://doi.org/10.1016/j.renene.2022.01.07610.1016/j.renene.2022.01.076 ↗
28. Neshat, M., Nezhad, M. M., Mirjalili, S., Piras, G., & Garcia, D. A. (2022). Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies. Energy Conversion and Management,259, 115590. https://doi.org/10.1016/j.enconman.2022.11559010.1016/j.enconman.2022.115590 ↗
29. Hu, W., Zhang, Q., & Ye, S. (2025). An enhanced dung beetle optimizer with multiple strategies for robot path planning. Scientific Reports,15, 4655. https://doi.org/10.1038/s41598-025-88347-z10.1038/s41598-025-88347-z ↗
30. Zhang, R., & Zhu, Y. (2023). Predicting the mechanical properties of heat-treated woods using optimization-algorithm-based BPNN. Forests,14(1), 935. https://doi.org/10.3390/f1405093510.3390/f14050935 ↗
31. Wu, C., Fu, J., Huang, X., Xu, X., & Meng, J. (2023). Lithium-ion battery health state prediction based on VMD and DBO-SVR. Energies,16(10), 3993. https://doi.org/10.3390/en1610399310.3390/en16103993 ↗
32. Xu, D., Li, Z., & Wang, W. (2024). An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy. Journal of Hydrology,629, 130558. https://doi.org/10.1016/j.jhydrol.2024.13055810.1016/j.jhydrol.2024.130558 ↗
33. Ali, M., Khan, A., & Rehman, Nu. (2018). Hybrid multiscale wind speed forecasting based on variational mode decomposition. International Transactions on Electrical Energy Systems,28(1), e2466. https://doi.org/10.1002/etep.246610.1002/etep.2466 ↗
34. Lu, J., Yue, J., Zhu, L., Wang, D., & Li, G. (2021). An improved variational mode decomposition method based on the optimization of salp swarm algorithm used for denoising of natural gas pipeline leakage signal. Measurement,185, 110107. https://doi.org/10.1016/j.measurement.2021.11010710.1016/j.measurement.2021.110107 ↗
35. Hu, Y., Guo, Y., & Fu, R. (2023). A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network. Energy,278, 127926. https://doi.org/10.1016/j.energy.2023.12792610.1016/j.energy.2023.127926 ↗
36. Moreno, S. R., Mariani, V. C., & dos Santos Coelho, L. (2021). Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian northeast. Renewable Energy,164, 1508–1526. https://doi.org/10.1016/j.renene.2020.10.12610.1016/j.renene.2020.10.126 ↗
37. Zhang, Y., Pan, G., Chen, B., Han, J., Zhao, Y., & Zhang, C. (2020). Short-term wind speed prediction model based on GA-ANN improved by VMD. Renewable Energy,156, 1373–1388. https://doi.org/10.1016/j.renene.2019.12.04710.1016/j.renene.2019.12.047 ↗
38. Zhang, Y., & Zhang, J. (2018). Volatility forecasting of crude oil market: A new hybrid method. Journal of Forecasting,37(7), 781–789. https://doi.org/10.1002/for.250210.1002/for.2502 ↗
39. Zhai, D., Zhang, T., Liang, G., & Liu, B. (2025). Research on crude oil futures price prediction methods: A perspective based on quantum deep learning. Energy,320, 135080. https://doi.org/10.1016/j.energy.2025.13508010.1016/j.energy.2025.135080 ↗
40. Zhang, J., Li, D., Hao, Y., & Tan, Z. (2018). A hybrid model using signal processing technology, econometric models and neural network for carbon spot price forecasting. Journal of Cleaner Production,204, 958–964. https://doi.org/10.1016/j.jclepro.2018.09.07110.1016/j.jclepro.2018.09.071 ↗
41. Liang, T., Zhao, Q., Lv, Q., & Sun, H. (2021). A novel wind speed prediction strategy based on BI-LSTM, MOOFADA and transfer learning for centralized control centers. Energy,230, 120904. https://doi.org/10.1016/j.energy.2021.12090410.1016/j.energy.2021.120904 ↗