A Hybrid Multivariate Multistep Wind-Speed Forecasting Model Based on a Deep-Learning Neural Network
Publication: Journal of Energy Engineering
Volume 150, Issue 6
Abstract
Predicting wind speed is a complex undertaking influenced not only by the wind-speed sequence itself but also by various meteorological factors. This paper introduces a novel multivariate deep-learning neural network prediction model that takes into account not only historical wind-speed data but also a series of meteorological features relevant to wind speed. The meteorological features associated with wind speed are initially extracted using the random forest algorithm (RF). Subsequently, Variational Mode Decomposition and Autocorrelation Function analysis are employed for noise reduction in the wind-speed series. Finally, the wind-speed series are predicted using a Gated Recurrent Unit (GRU) deep-learning neural network, and an Improved Sparrow Search Algorithm is proposed to optimize the four parameters of the GRU. To validate the predictive performance of the model, experimental data from three cities in China, Shenyang, Dalian, and Yingkou, are utilized. The experimental results demonstrate that our proposed model outperforms other models, as evidenced by four key performance indicators.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This article is supported by the Natural Science Foundation of Liaoning Province of China (No. 2020-MS-210) and the Science Research Project of Liaoning Education Department (No. LJKZ0143).
Author contributions: Donglai Wei: software, writing, and methodology; and Zhongda Tian: supervision, methodology, review, writing, and funding.
References
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (Oct): 5–32. https://doi.org/10.1023/A:1010933404324.
Cai, H., X. Jia, J. Feng, W. Li, Y.-M. Hsu, and J. Lee. 2020. “Gaussian process regression for numerical wind speed prediction enhancement.” Renewable Energy 146 (Feb): 2112–2123. https://doi.org/10.1016/j.renene.2019.08.018.
Chen, G., B. Tang, X. Zeng, P. Zhou, P. Kang, and H. Long. 2022. “Short-term wind speed forecasting based on long short-term memory and improved BP neural network.” Int. J. Electr. Power Energy Syst. 134 (Jan): 107365. https://doi.org/10.1016/j.ijepes.2021.107365.
Chen, Y., Z. Dong, Y. Wang, J. Su, Z. Han, D. Zhou, K. Zhang, Y. Zhao, and Y. Bao. 2021. “Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history.” Energy Convers. Manage. 227 (Jan): 113559. https://doi.org/10.1016/j.enconman.2020.113559.
Ding, Y., Z. Chen, H. Zhang, X. Wang, and Y. Guo. 2022. “A short-term wind power prediction model based on CEEMD and WOA-KELM.” Renewable Energy 189 (Apr): 188–198. https://doi.org/10.1016/j.renene.2022.02.108.
Dragomiretskiy, K., and D. Zosso. 2013. “Variational mode decomposition.” IEEE Trans. Signal Process. 62 (3): 531–544. https://doi.org/10.1109/TSP.2013.2288675.
Erdem, E., and J. Shi. 2011. “ARMA based approaches for forecasting the tuple of wind speed and direction.” Appl. Energy 88 (4): 1405–1414. https://doi.org/10.1016/j.apenergy.2010.10.031.
Feng, C., C. Liu, D. Jiang, D. Kong, and W. Zhang. 2023. “Multivariate anomaly detection and early warning framework for wind turbine condition monitoring using SCADA data.” J. Energy Eng. 149 (6): 04023040. https://doi.org/10.1061/JLEED9.EYENG-4843.
Fogno Fotso, H. R., C. V. Aloyem Kazé, and G. Djuidje Kenmoé. 2021. “A novel hybrid model based on weather variables relationships improving applied for wind speed forecasting.” Int. J. Energy Environ. Eng. 13 (1): 43–56. https://doi.org/10.1007/s40095-021-00408-x.
Hoolohan, V., A. S. Tomlin, and T. Cockerill. 2018. “Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data.” Renewable Energy 126 (Oct): 1043–1054. https://doi.org/10.1016/j.renene.2018.04.019.
Hu, H., L. Wang, and R. Tao. 2021. “Wind speed forecasting based on variational mode decomposition and improved echo state network.” Renewable Energy 164 (Feb): 729–751. https://doi.org/10.1016/j.renene.2020.09.109.
Hua, L., C. Zhang, T. Peng, C. Ji, and M. Shahzad Nazir. 2022. “Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction.” Energy Convers. Manage. 252 (Jan): 115102. https://doi.org/10.1016/j.enconman.2021.115102.
Jiang, P., and P. Li. 2017. “Research and application of a new hybrid wind speed forecasting model on BSO algorithm.” J. Energy Eng. 143 (1): 04016019. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000362.
Jiang, Z., J. Che, M. He, and F. Yuan. 2023. “A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition.” Renewable Energy 203 (Feb): 802–827. https://doi.org/10.1016/j.renene.2022.12.124.
Laxton, D. T. 2022. “Novel measure of innovation: Application to the onshore wind energy sector in the US.” J. Energy Eng. 148 (3): 04022012. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000838.
Li, H., J. Wang, H. Lu, and Z. Guo. 2018. “Research and application of a combined model based on variable weight for short term wind speed forecasting.” Renewable Energy 116 (Feb): 669–684. https://doi.org/10.1016/j.renene.2017.09.089.
Li, J., Z. Song, X. Wang, Y. Wang, and Y. Jia. 2022. “A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD.” Energy 251 (Jul): 123848. https://doi.org/10.1016/j.energy.2022.123848.
Li, J., J. Wang, and Z. Li. 2023a. “A novel combined forecasting system based on advanced optimization algorithm—A study on optimal interval prediction of wind speed.” Energy 264 (Feb): 126179. https://doi.org/10.1016/j.energy.2022.126179.
Li, M., Y. Yang, Z. He, X. Guo, R. Zhang, and B. Huang. 2023b. “A wind speed forecasting model based on multi-objective algorithm and interpretability learning.” Energy 269 (Apr): 126778. https://doi.org/10.1016/j.energy.2023.126778.
Liang, T., Q. Zhao, Q. Lv, and H. Sun. 2021. “A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers.” Energy 230 (Sep): 120904. https://doi.org/10.1016/j.energy.2021.120904.
Liu, H., H. Han, Y. Sun, G. Shi, M. Su, Z. Liu, H. Wang, and X. Deng. 2022. “Short-term wind power interval prediction method using VMD-RFG and Att-GRU.” Energy 251 (Jul): 123807. https://doi.org/10.1016/j.energy.2022.123807.
Liu, H., H. Wu, and Y. Li. 2020. “Multi-step wind speed forecasting model based on wavelet matching analysis and hybrid optimization framework.” Sustainable Energy Technol. Assess. 40 (Aug): 100745. https://doi.org/10.1016/j.seta.2020.100745.
Liu, X., Z. Lin, and Z. Feng. 2021. “Short-term offshore wind speed forecast by seasonal ARIMA—A comparison against GRU and LSTM.” Energy 227 (Jul): 120492. https://doi.org/10.1016/j.energy.2021.120492.
Memarzadeh, G., and F. Keynia. 2020. “A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets.” Energy Convers. Manage. 213 (Jun): 112824. https://doi.org/10.1016/j.enconman.2020.112824.
Mi, X., H. Liu, and Y. Li. 2019. “Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine.” Energy Convers. Manage. 180 (Jan): 196–205. https://doi.org/10.1016/j.enconman.2018.11.006.
Qiu, W., W. Zhang, G. Wang, Z. Guo, J. Zhao, and K. Ma. 2023. “Combined wind speed forecasting model based on secondary decomposition and quantile regression closed-form continuous-time neural network.” Int. J. Green Energy 21 (8): 1793–1814. https://doi.org/10.1080/15435075.2023.2269443.
Saeed, A., C. Li, Z. Gan, Y. Xie, and F. Liu. 2022. “A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution.” Energy 238 (Jan): 122012. https://doi.org/10.1016/j.energy.2021.122012.
Shang, Z., Z. He, Y. Chen, Y. Chen, and M. Xu. 2022. “Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization.” Energy 238 (Jan): 122024. https://doi.org/10.1016/j.energy.2021.122024.
Sibtain, M., H. Bashir, M. Nawaz, S. Hameed, M. Imran Azam, X. Li, T. Abbas, and S. Saleem. 2022. “A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network.” Energy Convers. Manage. 263 (Jul): 115703. https://doi.org/10.1016/j.enconman.2022.115703.
Sun, S., J. Fu, A. Li, and P. Zhang. 2021. “A new compound wind speed forecasting structure combining multi-kernel LSSVM with two-stage decomposition technique.” Soft Comput. 25 (2): 1479–1500. https://doi.org/10.1007/s00500-020-05233-8.
Tian, Z., and H. Chen. 2021. “A novel decomposition-ensemble prediction model for ultra-short-term wind speed.” Energy Convers. Manage. 248 (Nov): 114775. https://doi.org/10.1016/j.enconman.2021.114775.
Tian, Z., H. Li, and F. Li. 2021. “A combination forecasting model of wind speed based on decomposition.” Energy Rep. 7 (Nov): 1217–1233. https://doi.org/10.1016/j.egyr.2021.02.002.
Wang, H., S. Han, Y. Liu, J. Yan, and L. Li. 2019. “Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system.” Appl. Energy 237 (Mar): 1–10. https://doi.org/10.1016/j.apenergy.2018.12.076.
Wang, J., and J. Hu. 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 (Dec): 41–56. https://doi.org/10.1016/j.energy.2015.08.045.
Wang, J., X. Niu, L. Zhang, Z. Liu, and X. Huang. 2024. “A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies.” Expert Syst. Appl. 241 (May): 122487. https://doi.org/10.1016/j.eswa.2023.122487.
Wu, C., J. Wang, X. Chen, P. Du, and W. Yang. 2020. “A novel hybrid system based on multi-objective optimization for wind speed forecasting.” Renewable Energy 146 (Feb): 149–165. https://doi.org/10.1016/j.renene.2019.04.157.
Wu, H., K. Meng, D. Fan, Z. Zhang, and Q. Liu. 2022. “Multistep short-term wind speed forecasting using transformer.” Energy 261 (Dec): 125231. https://doi.org/10.1016/j.energy.2022.125231.
Xue, J., and B. Shen. 2020. “A novel swarm intelligence optimization approach: Sparrow search algorithm.” Syst. Sci. Control Eng. 8 (1): 22–34. https://doi.org/10.1080/21642583.2019.1708830.
Yu, M., D. Niu, T. Gao, K. Wang, L. Sun, M. Li, and X. Xu. 2023. “A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism.” Energy 269 (Apr): 126738. https://doi.org/10.1016/j.energy.2023.126738.
Zhang, D., G. Hu, J. Song, H. Gao, H. Ren, and W. Chen. 2024. “A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model.” Energy 288 (Feb): 129823. https://doi.org/10.1016/j.energy.2023.129823.
Zhang, W., L. Zhang, J. Wang, and X. Niu. 2020a. “Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting.” Appl. Energy 277 (Nov): 115561. https://doi.org/10.1016/j.apenergy.2020.115561.
Zhang, Y., Y. Chen, Z. Qi, S. Wang, J. Zhang, and F. Wang. 2022. “A hybrid forecasting system with complexity identification and improved optimization for short-term wind speed prediction.” Energy Convers. Manage. 270 (Oct): 116221. https://doi.org/10.1016/j.enconman.2022.116221.
Zhang, Y., G. Pan, B. Chen, J. Han, Y. Zhao, and C. Zhang. 2020b. “Short-term wind speed prediction model based on GA-ANN improved by VMD.” Renewable Energy 156 (Aug): 1373–1388. https://doi.org/10.1016/j.renene.2019.12.047.
Zhang, Z., J. Wang, D. Wei, T. Luo, and Y. Xia. 2023. “A novel ensemble system for short-term wind speed forecasting based on two-stage attention—Based recurrent neural network.” Renewable Energy 204 (Mar): 11–23. https://doi.org/10.1016/j.renene.2022.12.120.
Zhao, J., J. Wang, and F. Liu. 2016. “Multistep forecasting for short-term wind speed using an optimized extreme learning machine network with decomposition-based signal filtering.” J. Energy Eng. 142 (3): 04015036. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000291.
Zhu, S., X. Chen, X. Luo, K. Luo, J. Wei, J. Li, and Y. Xiong. 2022. “Enhanced probabilistic spatiotemporal wind speed forecasting based on deep learning, quantile regression, and error correction.” J. Energy Eng. 148 (2): 04022004. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000823.
Zhu, W. 2023. “Synergetic scheduling energy and reserve of wind farms for power systems with high-share wind power.” J. Energy Eng. 149 (2): 04023003. https://doi.org/10.1061/JLEED9.EYENG-4711.
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Published In
Journal of Energy Engineering
Volume 150 • Issue 6 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 16, 2024
Accepted: Jul 12, 2024
Published online: Oct 10, 2024
Published in print: Dec 1, 2024
Discussion open until: Mar 10, 2025
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