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Multivariable space-time correction for wind speed in numerical weather prediction (NWP) based on ConvLSTM and the prediction of probability interval

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Abstract

With the advent of the low-carbon era, wind power has become an indispensable energy source. Accurate day-ahead wind speed forecast is crucial for the power system to absorb wind power. Due to the influence of the spatiotemporal resolution and the error of forecasting itself, there is a certain error between the original wind speed of numerical weather prediction and the actual wind speed. Aiming to minimize this error as much as possible, this paper advocates using multivariable space-time information to jointly correct the wind speed in numerical weather prediction. Firstly, the correlation analysis experiments are carried out to demonstrate the feasibility of the idea. Then, the multivariable space-time experiment based on convolutional long short-term memory network is carried out, which greatly reduced the initial wind speed error in numerical weather prediction. At the same time, various methods are used for comparison. The experimental results show that the proposed method reduces the mean absolute error of the numerical weather prediction by 41.13% ~ 77.70% and reduces the root mean square error of the numerical weather prediction by 37.30% ~ 75.10% in 10 places, which is better than other comparison methods. Finally, to adapt to the regulatory needs of the power system, the probability interval predictions are carried out based on the corrected wind speed by the proposed method. The probability interval coverage probability reaches 0.924 ~ 0.937, while the probability interval averaged width reaches 1.869 ~ 2.198 in 10 places.

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If data and materials are needed, contact the email(darenyu2023@163.com) at any time, please. I will reply to you as soon as possible.

Notes

  1. https://nsrdb.nrel.gov/.

  2. https://www.ecmwf.int/.

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Funding

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Author information

Authors and Affiliations

  1. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Yunxiao Chen, Yilan Zhang, Jinfu Liu & Daren Yu

  2. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Mingliang Bai & Daren Yu

Authors
  1. Yunxiao Chen
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  2. Mingliang Bai
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  3. Yilan Zhang
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  4. Jinfu Liu
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  5. Daren Yu
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Contributions

Yunxiao Chen is responsible for the experiment process and article writing.

Mingliang Bai is responsible for experimental guidance and data collection.

Yilan Zhang is responsible for literature research.

Jinfu Liu is responsible for guiding the article format.

Daren Yu is responsible for the guidance of methods and ideas.

Corresponding author

Correspondence to Daren Yu.

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Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.

Additional information

Communicated by: H. Babaie

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Highlights

1. Multivariable space-time correction for wind speed in NWP based on ConvLSTM is first proposed.

2. The multivariate correlation, temporal correlation, and spatial correlation of wind speed have been proven.

3. Efficient probability interval prediction is performed.

4. The proposed method significantly improves original accuracy.

5. The experiment selects data from 10 places.

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Chen, Y., Bai, M., Zhang, Y. et al. Multivariable space-time correction for wind speed in numerical weather prediction (NWP) based on ConvLSTM and the prediction of probability interval. Earth Sci Inform 16, 1953–1974 (2023). https://doi.org/10.1007/s12145-023-01036-1

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  • DOI: https://doi.org/10.1007/s12145-023-01036-1

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