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Wind Power Interval Forecasting Based on Confidence Interval Optimization

Author

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  • Xiaodong Yu

    (School of Electrical Engineering, Shandong University, Jinan 250061, China
    School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, China)

  • Wen Zhang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Hongzhi Zang

    (Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250001, China)

  • Hao Yang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

Abstract

Most of the current wind power interval forecast methods are based on the assumption the point forecast error is subject to a known distribution (such as a normal distribution, beta distribution, etc.). The interval forecast of wind power is obtained after solving the confidence interval of the known distribution. However, this assumption does not reflect the truth because the distribution of error is random and does not necessary obey any known distribution. Moreover, the current method for calculating the confidence interval is only good for a known distribution. Therefore, those interval forecast methods cannot be applied generally, and the forecast quality is not good. In this paper, a general method is proposed to determine the optimal interval forecast of wind power. Firstly, the distribution of the point forecast error is found by using the non-parametric Parzen window estimation method which is suitable for the distribution of an arbitrary shape. Secondly, an optimal method is used to find the minimum confidence interval of arbitrary distribution. Finally the optimal forecast interval is obtained. Simulation results indicate that this method is not only generally applicable, but also has a better comprehensive evaluation index.

Suggested Citation

  • Xiaodong Yu & Wen Zhang & Hongzhi Zang & Hao Yang, 2018. "Wind Power Interval Forecasting Based on Confidence Interval Optimization," Energies, MDPI, vol. 11(12), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3336-:d:186601
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    References listed on IDEAS

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    1. 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.
    2. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
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    Citations

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    Cited by:

    1. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Ziran Yuan & Pengli Zhang & Bo Ming & Xiaobo Zheng & Lu Tian, 2023. "Joint Forecasting Method of Wind and Solar Outputs Considering Temporal and Spatial Correlation," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    3. Hu, Jianming & Luo, Qingxi & Tang, Jingwei & Heng, Jiani & Deng, Yuwen, 2022. "Conformalized temporal convolutional quantile regression networks for wind power interval forecasting," Energy, Elsevier, vol. 248(C).
    4. Pirhooshyaran, Mohammad & Scheinberg, Katya & Snyder, Lawrence V., 2020. "Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy," Energy, Elsevier, vol. 196(C).
    5. Li, Jingrui & Wang, Jiyang & Li, Zhiwu, 2023. "A novel combined forecasting system based on advanced optimization algorithm - A study on optimal interval prediction of wind speed," Energy, Elsevier, vol. 264(C).
    6. Jun Deng & Jun Suo & Jing Yang & Shutao Peng & Fangde Chi & Tong Wang, 2019. "Adaptive Damping Control Strategy of Wind Integrated Power System," Energies, MDPI, vol. 12(1), pages 1-18, January.
    7. Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.

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