https://orcid.org/0000-0003-1172-3397
Abstract
Wind speed forecasting plays a pivotal role in power prediction, daily operations, and optimal scheduling of wind farms. However, accurately predicting wind speed remains challenging due to data uncertainties and the inherent randomness of wind resources. This paper introduces a novel wind speed forecasting method by combining Bayesian discrete wavelet packet thresholding (BDWPT) into Gaussian Process Regression (GPR). The BDWPT method is first employed to adaptively remove noise from wind speed data, retaining the main trend characteristics of the time series while removing redundant information. The GPR model is then utilized to capture the remaining randomness and effectively predict future probabilistic trends in wind speed. Comparative studies using real-world wind farm data demonstrate the advantages of the proposed method in both one-step and multistep forecasting scenarios, showcasing its potential to enhance turbine design and power management under uncertain conditions.
Issue Section:
Special Papers
References
1.
Hutchinson
, M.
, and Z. F.
, 2023
, “Global Wind Report 2023
,” Global Wind Energy Council, Bruxelles, Belgium, accessed Dec. 19, 2024, https://gwec.net/globalwindreport2023/#download2.
Yuan
, X.
, Tan
, Q.
, Lei
, X.
, Yuan
, Y.
, and Wu
, X.
, 2017
, “Wind Power Prediction Using Hybrid Autoregressive Fractionally Integrated Moving Average and Least Square Support Vector Machine
,” Energy
, 129
, pp. 122
–137
.10.1016/j.energy.2017.04.0943.
De Giorgi
, M. G.
, Ficarella
, A.
, and Tarantino
, M.
, 2011
, “Assessment of the Benefits of Numerical Weather Predictions in Wind Power Forecasting Based on Statistical Methods
,” Energy
, 36
(7
), pp. 3968
–3978
.10.1016/j.energy.2011.05.0064.
Dayal
, K. K.
, Bellon
, G.
, Cater
, J. E.
, Kingan
, M. J.
, and Sharma
, R. N.
, 2021
, “High-Resolution Mesoscale Wind-Resource Assessment of Fiji Using the Weather Research and Forecasting (WRF) Model
,” Energy
, 232
, p. 121047
.10.1016/j.energy.2021.1210475.
Salcedo-Sanz
, S.
, Pérez-Bellido
, Á. M.
, Ortiz-García
, E. G.
, Portilla-Figueras
, A.
, Prieto
, L.
, and Paredes
, D.
, 2009
, “Hybridizing the Fifth Generation Mesoscale Model With Artificial Neural Networks for Short-Term Wind Speed Prediction
,” Renewable Energy
, 34
(6
), pp. 1451
–1457
.10.1016/j.renene.2008.10.0176.
Yang
, D.
, Wang
, W.
, and Hong
, T.
, 2022
, “A Historical Weather Forecast Dataset From the European Centre for Medium-Range Weather Forecasts (Ecmwf) for Energy Forecasting
,” Sol. Energy
, 232
, pp. 263
–274
.10.1016/j.solener.2021.12.0117.
Wang
, J.
, and Li
, Y.
, 2018
, “Multi-Step Ahead Wind Speed Prediction Based on Optimal Feature Extraction, Long Short Term Memory Neural Network and Error Correction Strategy
,” Appl. Energy
, 230
, pp. 429
–443
.10.1016/j.apenergy.2018.08.1148.
Erdem
, E.
, and Shi
, J.
, 2011
, “Arma Based Approaches for Forecasting the Tuple of Wind Speed and Direction
,” Appl. Energy
, 88
(4
), pp. 1405
–1414
.10.1016/j.apenergy.2010.10.0319.
Mauricio
, J. A.
, 1995
, “Exact Maximum Likelihood Estimation of Stationary Vector Arma Models
,” J. Am. Stat. Assoc.
, 90
(429
), pp. 282
–291
.10.1080/01621459.1995.1047651110.
Liang
, C.
, Liu
, Y.
, Zhou
, J.
, Yan
, J.
, and Lu
, Z.
, 2021
, “Wind Speed Prediction at Multi-Locations Based on Combination of Recurrent and Convolutional Neural Networks
,” Power Syst. Technol.
, 45
(2
), pp. 534
–542
.10.13335/j.1000-3673.pst.2020.076711.
Wei
, Y.
, and Xu
, X.
, 2019
, “Ultra-Short-Term Wind Speed Prediction Based on Lstm Long and Short-Term Memory Network
,” J. Electron. Meas. Instrum.
, 33
(2
), pp. 64
–71
.https://link.oversea.cnki.net/doi/10.13382/j.jemi.B180174712.
Zhaoyu
, L.
, Qian
, A.
, Yufan
, Z.
, and Fei
, X.
, 2019
, “A Lstm Neural Network Method Based on Attention Mechanism for Ultra Short-Term Load Forecasting
,” Distrib. Util.
, 36
(1
), pp. 17
–22
.https://link.oversea.cnki.net/doi/10.19421/j.cnki.1006-6357.2019.01.00313.
Gallego-Castillo
, C.
, Bessa
, R.
, Cavalcante
, L.
, and Lopez-Garcia
, O.
, 2016
, “On-Line Quantile Regression in the Rkhs (Reproducing Kernel Hilbert Space) for Operational Probabilistic Forecasting of Wind Power
,” Energy
, 113
, pp. 355
–365
.10.1016/j.energy.2016.07.05514.
Thorarinsdottir
, T. L.
, and Gneiting
, T.
, 2010
, “Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics by Using Heteroscedastic Censored Regression
,” J. R. Stat. Soc. Ser. A: Stat. Soc.
, 173
(2
), pp. 371
–388
.10.1111/j.1467-985X.2009.00616.x15.
Errouissi
, R.
, Cardenas-Barrera
, J.
, Meng
, J.
, Castillo-Guerra
, E.
, Gong
, X.
, and Chang
, L.
, 2015
, “Bootstrap Prediction Interval Estimation for Wind Speed Forecasting
,” IEEE Energy Conversion Congress and Exposition (ECCE),
Montreal, QC, Canada, Sept. 20–24, pp. 1919
–1924
.10.1109/ECCE.2015.730993116.
Yan
, J.
, Liu
, Y.
, Han
, S.
, Wang
, Y.
, and Feng
, S.
, 2015
, “Reviews on Uncertainty Analysis of Wind Power Forecasting
,” Renewable Sustainable Energy Rev.
, 52
, pp. 1322
–1330
.10.1016/j.rser.2015.07.19717.
Wei
, W.
, Wu
, J.
, Yu
, Y.
, Niu
, T.
, and Deng
, X.
, 2021
, “Uncertainty Quantification Analysis of Wind Power: A Data-Driven Monitoring-Forecasting Framework
,” IEEE Access
, 9
, pp. 84403
–84416
.10.1109/ACCESS.2021.308658318.
Lin
, Z.
, Liu
, X.
, and Collu
, M.
, 2020
, “Wind Power Prediction Based on High-Frequency Scada Data Along With Isolation Forest and Deep Learning Neural Networks
,” Int. J. Electr. Power Energy Syst.
, 118
, p. 105835
.10.1016/j.ijepes.2020.10583519.
Wang
, X.
, Li
, Z.
, Zhang
, J.
, Liu
, H.
, Qiu
, C.
, and Cai
, X.
, 2019
, “An Lstm-Attention Wind Power Prediction Method Considering Multiple Factors
,” 8th Renewable Power Generation Conference (RPG 2019
), Shanghai, China, Oct. 24–25, pp. 1
–7
.10.1049/cp.2019.059720.
Aghajani
, A.
, Kazemzadeh
, R.
, and Ebrahimi
, A.
, 2016
, “A Novel Hybrid Approach for Predicting Wind Farm Power Production Based on Wavelet Transform, Hybrid Neural Networks and Imperialist Competitive Algorithm
,” Energy Convers. Manage.
, 121
, pp. 232
–240
.10.1016/j.enconman.2016.05.02421.
Chitsaz
, H.
, Amjady
, N.
, and Zareipour
, H.
, 2015
, “Wind Power Forecast Using Wavelet Neural Network Trained by Improved Clonal Selection Algorithm
,” Energy Convers. Manage.
, 89
, pp. 588
–598
.10.1016/j.enconman.2014.10.00122.
Abdoos
, A. A.
, 2016
, “A New Intelligent Method Based on Combination of Vmd and Elm for Short Term Wind Power Forecasting
,” Neurocomputing
, 203
, pp. 111
–120
.10.1016/j.neucom.2016.03.05423.
Wang
, Y.
, and Wu
, L.
, 2016
, “On Practical Challenges of Decomposition-Based Hybrid Forecasting Algorithms for Wind Speed and Solar Irradiation
,” Energy
, 112
, pp. 208
–220
.10.1016/j.energy.2016.06.07524.
Xu
, S.
, Jiang
, X.
, Huang
, J.
, Yang
, S.
, and Wang
, X.
, 2016
, “Bayesian Wavelet Pca Methodology for Turbomachinery Damage Diagnosis Under Uncertainty
,” Mech. Syst. Signal Process.
, 80
, pp. 1
–18
.10.1016/j.ymssp.2016.04.03125.
Jiang
, X.
, Mahadevan
, S.
, and Adeli
, H.
, 2007
, “Bayesian Wavelet Packet Denoising for Structural System Identification
,” Struct. Control Health Monit.: Official J. Int. Assoc. Struct. Control Monit.
, 14
(2
), pp. 333
–356
.10.1002/stc.16126.
Zhang
, Z.
, Ye
, L.
, Qin
, H.
, Liu
, Y.
, Wang
, C.
, Yu
, X.
, Yin
, X.
, and Li
, J.
, 2019
, “Wind Speed Prediction Method Using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression
,” Appl. Energy
, 247
, pp. 270
–284
.10.1016/j.apenergy.2019.04.047Copyright © 2025 by ASME
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