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Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

Author

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  • Rongsheng Liu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Minfang Peng

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Xianghui Xiao

    (College of Automation, Foshan University, Foshan 52800, China)

Abstract

In order to improve the accuracy of wind power prediction (WPP), we propose a WPP based on multivariate phase space reconstruction (MPSR) and multivariate linear regression (MLR). Firstly, the multivariate time series (TS) are constructed through reasonable selection of wind power and weather factors, which are closely associated with wind power. Secondly, the phase space of the multivariate time series is reconstructed based on the chaos theory and C-C method. Thirdly, an auto regression model for multivariate phase space is created by regarding phase variables as state variables, and the very-short-term wind power is predicted by using a multi-linear regression algorithm. Finally, a parallel algorithm based on map/reduce is presented to improve computing speed. A cloud computing platform, Hadoop consisting of five nodes, is established as a matter of convenience, followed by the prediction of wind power of a wind farm in the Hunan province of China. The experimental results show that the model based on MPSR and MLR is more accurate than both the continuous method and the simple approximation method, and the parallel algorithm based on map/reduce effectively accelerates the computing speed.

Suggested Citation

  • Rongsheng Liu & Minfang Peng & Xianghui Xiao, 2018. "Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression," Energies, MDPI, vol. 11(10), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2763-:d:175798
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    References listed on IDEAS

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    Cited by:

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    2. Nan Yang & Yu Huang & Dengxu Hou & Songkai Liu & Di Ye & Bangtian Dong & Youping Fan, 2019. "Adaptive Nonparametric Kernel Density Estimation Approach for Joint Probability Density Function Modeling of Multiple Wind Farms," Energies, MDPI, vol. 12(7), pages 1-15, April.
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    4. Tian, Zhongda, 2020. "Chaotic characteristic analysis of network traffic time series at different time scales," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
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    6. Chen, Hao, 2022. "Cluster-based ensemble learning for wind power modeling from meteorological wind data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    8. Teuvo Suntio & Tuomas Messo, 2019. "Power Electronics in Renewable Energy Systems," Energies, MDPI, vol. 12(10), pages 1-5, May.
    9. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).

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