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Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression

Author

Listed:
  • Zhang, Zhendong
  • Ye, Lei
  • Qin, Hui
  • Liu, Yongqi
  • Wang, Chao
  • Yu, Xiang
  • Yin, Xingli
  • Li, Jie

Abstract

Wind energy has received more and more attention around the world since it is a kind of clean, economical and renewable energy. However, the strong randomness of the wind speed makes wind power difficult to integrate into the power grid. Obtaining reliable high-quality wind speed prediction results is very important for the planning and application of wind energy. In this study, Shared Weight Long Short-Term Memory Network (SWLSTM) is proposed to decrease the number of variables that need to be optimized and the training time of Long Short-Term Memory Network (LSTM) without significantly reducing prediction accuracy. Furthermore, a new hybrid model combined SWLSTM and GPR, called SWLSTM-GPR, is proposed to obtain reliable wind speed probabilistic prediction result. SWLSTM-GPR is applied to four wind speed prediction cases in Inner Mongolia, China and compared with the state-of-the-art wind speed prediction methods from four aspects: point prediction accuracy, interval prediction suitability, probability prediction comprehensive performance and training time. The reliability test of SWLSTM-GPR guarantees that the prediction results are reliable and convincing. The experimental results show that SWLSTM-GPR can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction problems.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:270-284
    DOI: 10.1016/j.apenergy.2019.04.047
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    References listed on IDEAS

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    1. Kiplangat, Dennis C. & Asokan, K. & Kumar, K. Satheesh, 2016. "Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition," Renewable Energy, Elsevier, vol. 93(C), pages 38-44.
    2. Yuan, Xiaohui & Tan, Qingxiong & Lei, Xiaohui & Yuan, Yanbin & Wu, Xiaotao, 2017. "Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine," Energy, Elsevier, vol. 129(C), pages 122-137.
    3. 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.
    4. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
    5. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    6. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    7. 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.
    8. Li, Ranran & Jin, Yu, 2018. "A wind speed interval prediction system based on multi-objective optimization for machine learning method," Applied Energy, Elsevier, vol. 228(C), pages 2207-2220.
    9. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    10. Zhang, Jie & Draxl, Caroline & Hopson, Thomas & Monache, Luca Delle & Vanvyve, Emilie & Hodge, Bri-Mathias, 2015. "Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods," Applied Energy, Elsevier, vol. 156(C), pages 528-541.
    11. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
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