Enhanced Probabilistic Spatiotemporal Wind Speed Forecasting Based on Deep Learning, Quantile Regression, and Error Correction
Publication: Journal of Energy Engineering
Volume 148, Issue 2
Abstract
Developing wind speed forecasting is a prerequisite for the safe and effective utilization of wind power. In this study, an enhanced spatiotemporal wind speed forecasting model is proposed for short-term wind speed prediction, which consists of convolutional long short-term memory network, quantile regression, and error correction modules. The model makes use of the powerful time-series mining ability of long short-term memory (LSTM) and the measurement of variable uncertainty by quantile regression so that the model has the advantages of advanced certainty and uncertainty prediction at the same time. In addition, the error correction module is added to further improve the forecast accuracy. The proposed model has been validated in three large-scale regions in the United States and compared with three other state-of-the-art models. In the deterministic prediction, compared with the best-performing LSTM among the baseline models, the mean absolute error and root mean square error are reduced by 30.71% and 26.99%, respectively. In probabilistic prediction, the proposed model performs better than Gaussian process regression with higher reliability. The results of statistical testing demonstrate that the proposed model can obtain both accurate deterministic prediction and reliable probabilistic prediction. This indicates that the model has advantages in the spatiotemporal prediction of large-scale regions.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, specifically including the raw data and model part of the code.
References
Allen, D., A. Tomlin, C. Bale, A. Skea, S. Vosper, and M. Gallani. 2017. “A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data.” Appl. Energy 208 (Dec): 1246–1257. https://doi.org/10.1016/j.apenergy.2017.09.029.
Bauer, M., M. van der Wilk, and C. E. Rasmussen. 2016. “Understanding probabilistic sparse Gaussian process approximations.” Preprint, submitted June 15, 2016. https://arxiv.org/abs/1606.04820.
Blasone, R.-S., J. A. Vrugt, H. Madsen, D. Rosbjerg, B. A. Robinson, and G. A. Zyvoloski. 2008. “Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling.” Adv. Water Resour. 31 (4): 630–648. https://doi.org/10.1016/j.advwatres.2007.12.003.
Bui, T. D., J. Yan, and R. E. Turner. 2017. “A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation.” J. Mach. Learn. Res. 18 (1): 3649–3720.
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, Y., S. Zhang, W. Zhang, J. Peng, and Y. Cai. 2019. “Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting.” Energy Convers. Manage. 185 (Apr): 783–799. https://doi.org/10.1016/j.enconman.2019.02.018.
Constantinescu, E. M., V. M. Zavala, M. Rocklin, S. Lee, and M. Anitescu. 2010. “A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation.” IEEE Trans. Power Syst. 26 (1): 431–441. https://doi.org/10.1109/TPWRS.2010.2048133.
Duan, J., H. Zuo, Y. Bai, J. Duan, M. Chang, and B. Chen. 2021. “Short-term wind speed forecasting using recurrent neural networks with error correction.” Energy 217 (Feb): 119397. https://doi.org/10.1016/j.energy.2020.119397.
Ehsan, A., and Q. Yang. 2019. “State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review.” Appl. Energy 239 (Apr): 1509–1523. https://doi.org/10.1016/j.apenergy.2019.01.211.
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.
Fischer, T., and C. Krauss. 2018. “Deep learning with long short-term memory networks for financial market predictions.” Eur. J. Oper. Res. 270 (2): 654–669. https://doi.org/10.1016/j.ejor.2017.11.054.
Gal, Y., and Z. Ghahramani. 2016. “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning.” Preprint, submitted June 6, 2015. https://arxiv.org/abs/1506.02142.
Gielen, D., F. Boshell, D. Saygin, M. Bazilian, N. Wagner, and R. Gorini. 2019. “The role of renewable energy in the global energy transformation.” Energy Strategy Rev. 24 (Apr): 38–50. https://doi.org/10.1016/j.esr.2019.01.006.
Gneiting, T., and M. Katzfuss. 2014. “Probabilistic forecasting.” Annu. Rev. Stat. Appl. 1 (Jan): 125–151. https://doi.org/10.1146/annurev-statistics-062713-085831.
Hur, S.-H. 2021. “Short-term wind speed prediction using Extended Kalman filter and machine learning.” Energy Rep. 7 (Nov): 1046–1054. https://doi.org/10.1016/j.egyr.2020.12.020.
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, P., Z. Liu, X. Niu, and L. Zhang. 2021. “A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting.” Energy 217 (Feb): 119361. https://doi.org/10.1016/j.energy.2020.119361.
Jiang, Y., Z. Song, and A. Kusiak. 2013. “Very short-term wind speed forecasting with Bayesian structural break model.” Renewable Energy 50 (Feb): 637–647. https://doi.org/10.1016/j.renene.2012.07.041.
Kani, S. P., and M. Ardehali. 2011. “Very short-term wind speed prediction: A new artificial neural network–Markov chain model.” Energy Convers. Manage. 52 (1): 738–745. https://doi.org/10.1016/j.enconman.2010.07.053.
Kaur, T., S. Kumar, and R. Segal. 2016. “Application of artificial neural network for short term wind speed forecasting.” In Proc., Biennial Int. Conf. on Power and Energy Systems: Towards Sustainable Energy (PESTSE), 1–5. New York: IEEE.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2014. https://arxiv.org/abs/1412.6980.
Lago, J., G. Marcjasz, B. De Schutter, and R. Weron. 2021. “Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark.” Appl. Energy 293 (Jul): 116983. https://doi.org/10.1016/j.apenergy.2021.116983.
LeCun, Y., B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. “Backpropagation applied to handwritten zip code recognition.” Neural Comput. 1 (4): 541–551. https://doi.org/10.1162/neco.1989.1.4.541.
Li, R., and Y. Jin. 2018. “A wind speed interval prediction system based on multi-objective optimization for machine learning method.” Appl. Energy 228 (Oct): 2207–2220. https://doi.org/10.1016/j.apenergy.2018.07.032.
Liu, G., J. Zhou, B. Jia, F. He, Y. Yang, and N. Sun. 2019a. “Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method.” Appl. Energy 238 (Mar): 643–667. https://doi.org/10.1016/j.apenergy.2019.01.105.
Liu, H., X. Mi, Y. Li, Z. Duan, and Y. Xu. 2019b. “Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression.” Renewable Energy 143 (Dec): 842–854. https://doi.org/10.1016/j.renene.2019.05.039.
Liu, Y., H. Qin, Z. Zhang, S. Pei, Z. Jiang, Z. Feng, and J. Zhou. 2020. “Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model.” Appl. Energy 260 (Feb): 114259. https://doi.org/10.1016/j.apenergy.2019.114259.
Mishra, S., C. Bordin, K. Taharaguchi, and I. Palu. 2020. “Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature.” Energy Rep. 6 (Feb): 273–286. https://doi.org/10.1016/j.egyr.2019.11.009.
Mohammed, A. A., W. Yaqub, and Z. Aung. 2017. “Probabilistic forecasting of solar power: An ensemble learning approach.” In Proc., Int. Conf. on Intelligent Decision Technologies, 449–458. Cham, Switzerland: Springer.
Nowotarski, J., and R. Weron. 2018. “Recent advances in electricity price forecasting: A review of probabilistic forecasting.” Renewable Sustainable Energy Rev. 81 (Jan): 1548–1568. https://doi.org/10.1016/j.rser.2017.05.234.
Rasmussen, C. E., and C. K. I. Williams. 2006. Gaussian processes for machine learning. Cambridge, MA: MIT Press.
Rodrigues, F., and F. C. Pereira. 2020. “Beyond expectation: Deep joint mean and quantile regression for spatiotemporal problems.” IEEE Trans. Neural Networks Learn. Syst. 31 (12): 5377–5389. https://doi.org/10.1109/TNNLS.2020.2966745.
Routray, A., K. D. Mistry, S. R. Arya, and B. Chittibabu. 2020. “Applied machine learning in wind speed prediction and loss minimization in unbalanced radial distribution system.” Energy Sources Part A. 1–21. https://doi.org/10.1080/15567036.2020.1859010.
Ruder, S. 2017. “An overview of multi-task learning in deep neural networks.” Preprint, submitted June 15, 2017. https://arxiv.org/abs/1706.05098.
Sanandaji, B. M., A. Tascikaraoglu, K. Poolla, and P. Varaiya. 2015. “Low-dimensional models in spatio-temporal wind speed forecasting.” In Proc., 2015 American Control Conf. (ACC), 4485–4490. New York: IEEE.
Shewalkar, A., D. Nyavanandi, and S. A. Ludwig. 2019. “Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU.” J. Artif. Intell. Soft Comput. Res. 9 (4): 235–245. https://doi.org/10.2478/jaiscr-2019-0006.
Shi, J., Z. Ding, W.-J. Lee, Y. Yang, Y. Liu, and M. Zhang. 2013. “Hybrid forecasting model for very-short term wind power forecasting based on grey relational analysis and wind speed distribution features.” IEEE Trans. Smart Grid 5 (1): 521–526. https://doi.org/10.1109/TSG.2013.2283269.
Wang, K., W. Fu, T. Chen, B. Zhang, D. Xiong, and P. Fang. 2020. “A compound framework for wind speed forecasting based on comprehensive feature selection, quantile regression incorporated into convolutional simplified long short-term memory network and residual error correction.” Energy Convers. Manage. 222 (Oct): 113234. https://doi.org/10.1016/j.enconman.2020.113234.
Wei, H., H. Shao, and X. Deng. 2016. “Using a model structure selection technique to forecast short-term wind speed for a wind power plant in North China.” J. Energy Eng. 142 (1): 04015005. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000269.
Weron, R. 2014. “Electricity price forecasting: A review of the state-of-the-art with a look into the future.” Int. J. Forecasting 30 (4): 1030–1081. https://doi.org/10.1016/j.ijforecast.2014.08.008.
Xingjian, S., Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo. 2015. “Convolutional LSTM network: A machine learning approach for precipitation nowcasting.” Preprint, submitted June 13, 2015. https://arxiv.org/abs/1506.04214.
Zeng, J., and W. Qiao. 2011. “Support vector machine-based short-term wind power forecasting.” In Proc., 2011 IEEE/PES Power Systems Conf. and Exposition, 1–8. New York: IEEE.
Zhang, Z. 2018. “Improved adam optimizer for deep neural networks.” In Proc., 2018 IEEE/ACM 26th Int. Symp. on Quality of Service (IWQoS), 1–2. New York: IEEE.
Zhang, Z., H. Qin, Y. Liu, L. Yao, X. Yu, J. Lu, Z. Jiang, and Z. Feng. 2019a. “Wind speed forecasting based on quantile regression minimal gated memory network and kernel density estimation.” Energy Convers. Manage. 196 (Sep): 1395–1409. https://doi.org/10.1016/j.enconman.2019.06.024.
Zhang, Z., L. Ye, H. Qin, Y. Liu, C. Wang, X. Yu, X. Yin, and J. Li. 2019b. “Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression.” Appl. Energy 247 (Aug): 270–284. https://doi.org/10.1016/j.apenergy.2019.04.047.
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.
Zheng, L., B. Zhou, S. W. Or, Y. Cao, H. Wang, Y. Li, and K. W. Chan. 2021. “Spatio-temporal wind speed prediction of multiple wind farms using capsule network.” Renewable Energy 175 (Sep): 718–730. https://doi.org/10.1016/j.renene.2021.05.023.
Zhu, Q., J. Chen, L. Zhu, X. Duan, and Y. Liu. 2018. “Wind speed prediction with spatio–temporal correlation: A deep learning approach.” Energies 11 (4): 705. https://doi.org/10.3390/en11040705.
Zhu, S., X. Yuan, Z. Xu, X. Luo, and H. Zhang. 2019. “Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast.” Energy Convers. Manage. 198 (Oct): 111772. https://doi.org/10.1016/j.enconman.2019.06.083.
Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 148 • Issue 2 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 16, 2021
Accepted: Nov 18, 2021
Published online: Jan 19, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 19, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Shuang Zhu, Maoyu Zhang, Chao Wang, Jun Guo, Xudong Chen, Mengfei Xie, A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction, Journal of Hydrologic Engineering, 10.1061/JHYEFF.HEENG-6091, 29, 4, (2024).
- Wenzhuo Zhu, Synergetic Scheduling Energy and Reserve of Wind Farms for Power Systems with High-Share Wind Power, Journal of Energy Engineering, 10.1061/JLEED9.EYENG-4711, 149, 2, (2023).