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Nonlinear fuzzy forecasting system for wind speed interval forecasting based on self-adaption feature selecting and Bi-LSTM

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Abstract

Accurate wind speed forecasting can effectively improve the balance of wind power generation and reduce the consumption of power system. However, many previous studies have only carried out wind speed point prediction or interval prediction, which may lead to incomplete analysis of wind speed. In this article, A nonlinear fuzzy forecasting system is designed for wind speed forecasting, and it is composed of four modules: the self-adaption feature selecting module, the point forecasting module, the fuzzy interval forecasting module and the evaluation module. In the first module, the weights of Intrinsic Mode Functions (IMFs) are calculated by the multi-objective optimization algorithm to reconstruct time series data, which can enhance the stability of forecasting effectively. In the point forecasting module, the nonlinear combined model based on Bi-directional Long Short-Term Memory (Bi-LSTM) are used for reducing the forecasting error and assisting the formulation of power dispatching strategy. Meanwhile, the fuzzy interval forecasting can analyze the wind speed fluctuation and help to arrange the rotating reserve. The results show the nonlinear hybrid forecasting system can not only give accurate wind speed forecasting for wind power generation management by point forecasting, but also provide effective reference for the grid rotating reserve through interval forecasting.

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Data availability

This paper chooses the data from two wind farms to test the established nonlinear hybrid forecasting system., one of which is named Sotavento in the southwest of Europe, in Galicia, Spain (N: 43, 354377°, W: 7, 881213°) and another farm is located in Penglai (37.48 N, 120.45 E).

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Funding

This research was supported by the National Natural Science Foundation of China (Grant No. 71671029).

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Authors and Affiliations

  1. School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, People’s Republic of China

    Haipeng Zhang & Jianzhou Wang

  2. Dalian Ocean University, Dalian, 116025, People’s Republic of China

    Qiwei Li

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  1. Haipeng Zhang
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  2. Jianzhou Wang
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Contributions

HZ: Methodology, Software, Writing—Original Draft, Formal analysis, Original draft preparation, Visualization; JW: Conceptualization, Visualization, Writing—Review & Editing, Supervision, Reviewing and Editing, Funding acquisition; QL: Data curation, Investigation. Software, Reviewing and Editing, Data curation, Reviewing and Editing.

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Correspondence to Jianzhou Wang.

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Zhang, H., Wang, J. & Li, Q. Nonlinear fuzzy forecasting system for wind speed interval forecasting based on self-adaption feature selecting and Bi-LSTM. SIViP 18, 1249–1258 (2024). https://doi.org/10.1007/s11760-023-02759-w

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  • DOI: https://doi.org/10.1007/s11760-023-02759-w

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