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Multi-step interval prediction of ultra-short-term wind power based on CEEMDAN-FIG and CNN-BiLSTM

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Abstract

Aiming at the uncertainty of wind power and the low accuracy of multi-step interval prediction, an ultra-short-term wind power multi-step interval prediction method based on complete ensemble empirical mode decomposition with adaptive noise-fuzzy information granulation (CEEMDAN-FIG) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed. Firstly, the CEEMDAN is used to decompose the wind power time series into several sub-components to reduce the non-stationary characteristics of the wind power time series. Then, different components are selected for FIG, and the maximum value sequence, average value sequence, minimum value sequence gotten from FIG, and the remaining components without FIG are combined with the wind speed data, wind direction data, and the temperature data. They all are input into the CNN-BiLSTM combined prediction model to obtain the initial wind power prediction interval. The prediction results of the maximum value sequence, the average value sequence, and the minimum value sequence are respectively superimposed on the prediction results of the remaining components to obtain the upper limit, point prediction, and lower limit of the initial prediction interval. Finally, the improved coverage width criterion is used as the objective function to optimize the interval, and the forecast interval of wind power under a given confidence level is generated. Taking the actual operating data of a certain unit of a wind farm as an example, the validity of the proposed model is verified.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This study was funded by the Fundamental Research Funds for the Central Universities (2017MS133), the General Project of Beijing Municipal Natural Science Foundation (3202027), and the Shenzhen Science and Technology Plan Project (KCXFZ20201221173402007).

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, Hebei, China

    Zheng Zhao, Honggang Nan, Zihan Liu & Yuebo Yu

Authors
  1. Zheng Zhao
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  2. Honggang Nan
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  3. Zihan Liu
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  4. Yuebo Yu
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Contributions

Zheng Zhao: conceptualization, methodology. Honggang Nan: methodology, performed the experiments. Zihan Liu and Yuebo Yu: writing review and editing.

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Correspondence to Zheng Zhao.

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Zhao, Z., Nan, H., Liu, Z. et al. Multi-step interval prediction of ultra-short-term wind power based on CEEMDAN-FIG and CNN-BiLSTM. Environ Sci Pollut Res 29, 58097–58109 (2022). https://doi.org/10.1007/s11356-022-19885-6

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  • DOI: https://doi.org/10.1007/s11356-022-19885-6

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