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Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model

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  • Liu, Yongqi
  • Qin, Hui
  • Zhang, Zhendong
  • Pei, Shaoqian
  • Jiang, Zhiqiang
  • Feng, Zhongkai
  • Zhou, Jianzhong

Abstract

Reliable and accurate probabilistic forecasting of wind speed is of vital importance for the utilization of wind energy and operation of power systems. In this paper, a probabilistic spatiotemporal deep learning model for wind speed forecasting is proposed. The underlying wind turbines are embedded into a grid space, which fully expresses the spatiotemporal variation process of the airflow. Thus, advanced image recognition methods can be employed to solve the spatiotemporal wind speed forecasting problem. The proposed model is based on a spatial–temporal neural network (STNN) and variational Bayesian inference. The proposed STNN combines the convolutional GRU model and 3D Convolutional Neural Network and uses an encoding-forecasting structure to generate the spatiotemporal predictions. Variational Bayesian inference is employed to obtain the approximated posterior parameter distribution of the model and determine the probability of the prediction. The proposed model is applied in two real-world case studies in United States. The experimental results demonstrate that the proposed model significantly outperforms other models in both forecast skill and forecast reliability. The uncertainty estimation is also shown and it demonstrates that the proposed model is able to provide effective uncertainty estimation in both the time level and space level.

Suggested Citation

  • Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919319464
    DOI: 10.1016/j.apenergy.2019.114259
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    References listed on IDEAS

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    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    3. Liu, Guangbiao & Zhou, Jianzhong & Jia, Benjun & He, Feifei & Yang, Yuqi & Sun, Na, 2019. "Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method," Applied Energy, Elsevier, vol. 238(C), pages 643-667.
    4. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    5. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    6. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
    7. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    8. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
    9. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    11. Qin, Yong & Li, Kun & Liang, Zhanhao & Lee, Brendan & Zhang, Fuyong & Gu, Yongcheng & Zhang, Lei & Wu, Fengzhi & Rodriguez, Dragan, 2019. "Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal," Applied Energy, Elsevier, vol. 236(C), pages 262-272.
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