Uncertainty prediction of wind speed based on improved multi-strategy hybrid models

Xinyi Xu; Shaojuan Ma; Cheng Huang; Xinyi Xu; Shaojuan Ma; Cheng Huang

doi:10.3934/era.2025016

Electronic Research Archive

2025, Volume 33, Issue 1: 294-326. doi: 10.3934/era.2025016

Previous Article Next Article

Research article

Uncertainty prediction of wind speed based on improved multi-strategy hybrid models

1.
School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China
2.
Ningxia Key Laboratory of Intelligent Information and Big Data Processing, North Minzu University, Yinchuan 750021, China

Received: 20 November 2024 Revised: 12 January 2025 Accepted: 20 January 2025 Published: 23 January 2025

Accurate interval prediction of wind speed plays a vital role in ensuring the efficiency and stability of wind power generation. Due to insufficient traditional wind speed interval prediction methods for mining nonlinear features, in this paper, a novel interval prediction method was proposed by combining improved wavelet threshold and deep learning (BiTCN-BiGRU) with the nutcracker optimization algorithm (NOA). First, NOA was used to optimize the wavelet transform (WT) and BiTCN-BiGRU. Second, we applied NOA-WT to smooth the wind speed data. Then, to capture nonlinear features of time series, phase space reconstruction (PSR) was utilized to identify chaotic characteristics of the processed data. Finally, the NOA-BiTCN-BiGRU model was built to perform wind speed interval prediction. Under the same hyperparameters and network structure settings, a comparison with other deep learning methods showed that the prediction interval coverage probability (PICP) and prediction interval mean width (PIMW) of NOA-WT-BiTCN-BiGRU model achieves the best balance, with good prediction accuracy and generalization performance. This research can provide reference and guidance for nonlinear time-series interval prediction in the real world.
- wind speed interval prediction,
- chaotic time series,
- nutcracker optimization algorithm,
- wavelet threshold,
- BiTCN-BiGRU
Citation: Xinyi Xu, Shaojuan Ma, Cheng Huang. Uncertainty prediction of wind speed based on improved multi-strategy hybrid models[J]. Electronic Research Archive, 2025, 33(1): 294-326. doi: 10.3934/era.2025016

Related Papers:

Abstract

Accurate interval prediction of wind speed plays a vital role in ensuring the efficiency and stability of wind power generation. Due to insufficient traditional wind speed interval prediction methods for mining nonlinear features, in this paper, a novel interval prediction method was proposed by combining improved wavelet threshold and deep learning (BiTCN-BiGRU) with the nutcracker optimization algorithm (NOA). First, NOA was used to optimize the wavelet transform (WT) and BiTCN-BiGRU. Second, we applied NOA-WT to smooth the wind speed data. Then, to capture nonlinear features of time series, phase space reconstruction (PSR) was utilized to identify chaotic characteristics of the processed data. Finally, the NOA-BiTCN-BiGRU model was built to perform wind speed interval prediction. Under the same hyperparameters and network structure settings, a comparison with other deep learning methods showed that the prediction interval coverage probability (PICP) and prediction interval mean width (PIMW) of NOA-WT-BiTCN-BiGRU model achieves the best balance, with good prediction accuracy and generalization performance. This research can provide reference and guidance for nonlinear time-series interval prediction in the real world.

References

[1]	V. Vigneshwar, S. Y. Krishnan, R. S. Kishna, R. Srinath, B. Ashok, K. Nanthagopal, Comprehensive review of Calophyllum inophyllum as a feasible alternate energy for CI engine applications, Renewable Sustainable Energy Rev., 115 (2019), 109397. https://doi.org/10.1016/j.rser.2019.109397 doi: 10.1016/j.rser.2019.109397
[2]	M. DeCastro, S. Salvador, M. Gómez-Gesteira, X. Costoya, D. Carvalho, F. J. Sanz-Larruga, et al., Europe, China and the United States: Three different approaches to the development of offshore wind energy, Renewable Sustainable Energy Rev., 109 (2019), 55–70. https://doi.org/10.1016/j.rser.2019.04.025 doi: 10.1016/j.rser.2019.04.025
[3]	J. Chen, G. Q. Zeng, W. Zhou, W. Du, K. D. Lu, Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization, Energy Convers. Manage., 165 (2018), 681–695. https://doi.org/10.1016/j.enconman.2018.03.098 doi: 10.1016/j.enconman.2018.03.098
[4]	D. L. Donoho, De-noising by soft-thresholding, IEEE Trans. Inf. Theory, 41 (1995), 613–627. https://doi.org/10.1109/18.382009 doi: 10.1109/18.382009
[5]	D. L. Donoho, I. M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, J. Am. Stat. Assoc., 90 (1995), 1200–1224. https://doi.org/10.1515/9781400827268.833 doi: 10.1515/9781400827268.833
[6]	S. Wu, L. Jia, Y. Liu, Ultra-short-term wind energy prediction based on wavelet denoising and multivariate LSTM, in 2021 Power System and Green Energy Conference (PSGEC), IEEE, (2021), 443–447. https://doi.org/10.1109/psgec51302.2021.9541909
[7]	L. Lian, K. He, Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine, Wind Eng., 46 (2022), 866–885. https://doi.org/10.1177/0309524x211056822 doi: 10.1177/0309524x211056822
[8]	I. Karijadi, S. Y. Chou, A. Dewabharata, Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method, Renewable Energy, 218 (2023), 119357. https://doi.org/10.1016/j.renene.2023.119357 doi: 10.1016/j.renene.2023.119357
[9]	S. E. Kelly, Gibbs phenomenon for wavelets, Appl. Comput. Harmon. Anal., 3 (1996), 72–81. https://doi.org/10.1006/acha.1996.0006 doi: 10.1006/acha.1996.0006
[10]	Y. Lin, J. Cai, A new threshold function for signal denoising based on wavelet transform, in 2010 International Conference on Measuring Technology and Mechatronics Automation, IEEE, (2010), 200–203. https://doi.org/10.1109/icmtma.2010.347
[11]	L. Su, G. Zhao, R. Zhang, Translation-invariant wavelet de-noising method with improved thresholding, in IEEE International Symposium on Communications and Information Technology, IEEE, (2005), 619–622. https://doi.org/10.1109/iscit.2005.1566931
[12]	Z. Peng, S. Peng, L. Fu, B. Lu, J. Tang, K. Wang, et al., A novel deep learning ensemble model with data denoising for short-term wind speed forecasting, Appl. Comput. Harmon. Anal., 207 (2020), 112524. https://doi.org/10.1016/j.enconman.2020.112524 doi: 10.1016/j.enconman.2020.112524
[13]	L. Jing-Yi, L. Hong, Y. Dong, Z. Yan-Sheng, A new wavelet threshold function and denoising application, Math. Probl. Eng., 1 (2016), 3195492. https://doi.org/10.1155/2016/3195492 doi: 10.1155/2016/3195492
[14]	Y. Wang, C. Xu, Y. Wang, X. Cheng, A comprehensive diagnosis method of rolling bearing fault based on CEEMDAN-DFA-improved wavelet threshold function and QPSO-MPE-SVM, Entropy, 23 (2021), 1142. https://doi.org/10.3390/e23091142 doi: 10.3390/e23091142
[15]	Y. Qian, Image denoising algorithm based on improved wavelet threshold function and median filter, in 2018 IEEE 18th International Conference on Communication Technology (ICCT), IEEE, (2018), 1197–1202. https://doi.org/10.1109/icct.2018.8599921
[16]	H. H. Goh, L. Liao, D. Zhang, W. Dai, C. S. Lim, T. A. Kurniawan, et al., Denoising transient power quality disturbances using an improved adaptive wavelet threshold method based on energy optimization, Energies, 15 (2022), 3081. https://doi.org/10.3390/en15093081 doi: 10.3390/en15093081
[17]	Y. Qiao, Q. Li, H. Qian, X. Song, Seismic signal denoising method based on CEEMD and improved wavelet threshold, in IOP Conference Series: Earth and Environmental Science, (2021), 012036. https://doi.org/10.1088/1755-1315/671/1/012036
[18]	C. Hu, F. Xing, S. Pan, R. Yuan, Y. Lv, Fault diagnosis of rolling bearings based on variational mode decomposition and genetic algorithm-optimized wavelet threshold denoising, Machines, 10 (2022), 649. https://doi.org/10.3390/machines10080649 doi: 10.3390/machines10080649
[19]	F. Ji, X. Cai, J. Zhang, Wind power prediction interval estimation method using wavelet-transform neuro-fuzzy network, J. Intell. Fuzzy Syst., 29 (2015), 2439–2445. https://doi.org/10.3233/ifs-151944 doi: 10.3233/ifs-151944
[20]	R. Li, Y. Jin, A wind speed interval prediction system based on multi-objective optimization for machine learning method, Appl. Energy, 228 (2018), 2207–2220. https://doi.org/10.1016/j.apenergy.2018.07.032 doi: 10.1016/j.apenergy.2018.07.032
[21]	Y. Zhang, G. Pan, Y. Zhao, Q. Li, F. Wang, Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution, Energy Convers. Manage., 224 (2020), 113346. https://doi.org/10.1016/j.enconman.2020.113346 doi: 10.1016/j.enconman.2020.113346
[22]	Z. Gan, C. Li, J. Zhou, G. Tang, Temporal convolutional networks interval prediction model for wind speed forecasting, Electr. Power Syst. Res., 191 (2021), 106865. https://doi.org/10.1016/j.epsr.2020.106865 doi: 10.1016/j.epsr.2020.106865
[23]	Y. Liu, H. Qin, Z. Zhang, S. Pei, Z. Jiang, Z. Feng, et al., Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model, Appl. Energy, 260 (2020), 114259. https://doi.org/10.1016/j.apenergy.2019.114259 doi: 10.1016/j.apenergy.2019.114259
[24]	X. Yuan, C. Chen, M. Jiang, Y. Yuan, Prediction interval of wind power using parameter optimized Beta distribution based LSTM model, Appl. Soft Comput., 82 (2019), 105550. https://doi.org/10.1016/j.asoc.2019.105550 doi: 10.1016/j.asoc.2019.105550
[25]	N. Pei, Y. Wu, R. Su, X. Li, Z. Wu, R. Li, et al., Interval prediction of the permeability of granite bodies in a high-level radioactive waste disposal site using LSTM-RNNs and probability distribution, Front. Earth Sci., 10 (2022), 835308. https://doi.org/10.3389/feart.2022.835308 doi: 10.3389/feart.2022.835308
[26]	K. Zhang, X. Yu, S. Liu, X. Dong, D. Li, H. Zang, et al., Wind power interval prediction based on hybrid semi-cloud model and nonparametric kernel density estimation, Energy Rep., 8 (2022), 1068–1078. https://doi.org/10.1016/j.egyr.2022.02.094 doi: 10.1016/j.egyr.2022.02.094
[27]	J. Wang, S. Wang, B. Zeng, H. Lu, A novel ensemble probabilistic forecasting system for uncertainty in wind speed, Appl. Energy, 313 (2022), 118796. https://doi.org/10.1016/j.apenergy.2022.118796 doi: 10.1016/j.apenergy.2022.118796
[28]	X. Peng, H. Wang, J. Lang, W. Li, Q. Xu, Z. Zhang, et al., EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning, Energy, 220 (2021), 119692. https://doi.org/10.1016/j.energy.2020.119692 doi: 10.1016/j.energy.2020.119692
[29]	J. Wang, S. Wang, Z. Li, Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression, Renewable Energy, 179 (2021), 1246–1261. https://doi.org/10.1016/j.renene.2021.07.113 doi: 10.1016/j.renene.2021.07.113
[30]	M. Abdel-Basset, R. Mohamed, M. Jameel, M. Abouhawwash, Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems, Knowl.-Based Syst., 262 (2023), 110248. https://doi.org/10.1016/j.knosys.2022.110248 doi: 10.1016/j.knosys.2022.110248
[31]	J. Zhou, X. Lu, Y. Xiao, J. Su, J. Lyu, Y. Ma, et al., Sdwpf: A dataset for spatial dynamic wind power forecasting challenge at kdd cup 2022, preprint, arXiv: 2208.04360.
[32]	L. Tang, J. Liang, CC method to phase space reconstruction based on multivariate time series, in 2011 2nd International Conference on Intelligent Control and Information Processing, IEEE, (2011), 438–441. https://doi.org/10.1109/icicip.2011.6008282
[33]	A. Wolf, J. B. Swift, H. L. Swinney, J. A. Vastano, Determining Lyapunov exponents from a time series, Physica D, 16 (1985), 285–317. https://doi.org/10.1007/bfb0086675 doi: 10.1007/bfb0086675
[34]	J. Xue, B. Shen, A novel swarm intelligence optimization approach: sparrow search algorithm, Syst. Sci. Control Eng., 8 (2020), 22–34. https://doi.org/10.1080/21642583.2019.1708830 doi: 10.1080/21642583.2019.1708830
[35]	S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software, 69 (2014), 46–61. https://doi.org/10.1201/9781003206477-8
[36]	S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)