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An improved GM(1,1) forecasting model based on Aquila Optimizer for wind power generation in Sichuan Province

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Abstract

With the rapid development of China's economy, wind resource development has important practical significance for alleviating environmental pollution problems in China. As China's clean energy province and western economic center, Sichuan's wind power industry is gradually starting from the 13th Five-Year Plan. Considering the volatility and periodic characteristics of wind power generation in Sichuan Province, this paper proposes an optimized seasonal grey model based on Aquila Optimizer. The proposed model selects dummy variables 1 and 0 to represent seasonal factors and perform seasonal classification of the sample data. According to the classification sequence, this paper constructs the grey forecasting model with optimized initial and background values by Aquila Optimizer. The proposed model predicts wind power generation in Sichuan Province and verifies its validity and rationality by comparing results with other selected methods. In training and test groups, the performance results of the proposed model are better. The mean absolute percentage error is 3.44% and 12.34%, and the root mean square error is 0.86% and 4.33%. Finally, this paper further provides policy advice and planning based on the prediction results for the future development of Sichuan Province's clean energy industry during the 14th Five-Year Plan period.

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Data availability

Data will be made available on reasonable request.

Abbreviations

AO:

Aquila Optimizer

AO-SGM(1,1):

Seasonal grey model(1,1) based on Aquila Optimizer

ARIMA:

Autoregressive integrated moving average model

DGM(1,1):

Discrete grey model(1,1)

GM(1,1):

Grey model(1,1)

HPF-GM(1,1):

Grey model(1,1) based on Hodrick–Prescott filter

IAGO:

Inverse acumulative generation

MAPE:

Mean absolute percentage error

PSO:

Particle swarm optimization

PSO-SGM(1,1):

Seasonal grey model(1,1) based on particle swarm optimization

RMSE:

Root mean square error

SARIMA:

Seasonal autoregressive integrated moving average model

SCGM(1,1):

Seasonal cycle grey model(1,1)

1-AGO:

First-order accumulation generation

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Funding

The research reported in this paper was partially supported by the National Natural Science Foundation of China (No. 71871106) and the Fundamental Research Funds for the Central Universities (Nos. JUSRP1809ZD; 2019JDZD06; JUSRP321016). the Key Project of Philosophy and Social Science Research in Universities of Jiangsu Province (No. 2018SJZDI051); the Major Projects of Philosophy and Social Science Research of Guizhou Province (No. 21GZZB32); Project of Chinese Academic Degrees and Graduate Education (No. 2020ZDB2); Major research project of the 14th Five-Year Plan for Higher Education Scientific Research of Jiangsu Higher Education Association (No. ZDGG02).

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Authors and Affiliations

  1. School of Business, Jiangnan University, Wuxi, 214122, Jiangsu Province, People’s Republic of China

    Youyang Ren, Lin Xia & Yuhong Wang

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  1. Youyang Ren
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  2. Lin Xia
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  3. Yuhong Wang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Youyang Ren, Lin Xia and Yuhong Wang. The first draft of the manuscript was written by Youyang Ren. All authors read and revised the final manuscript.

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Correspondence to Yuhong Wang.

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Ren, Y., Xia, L. & Wang, Y. An improved GM(1,1) forecasting model based on Aquila Optimizer for wind power generation in Sichuan Province. Soft Comput 28, 8785–8805 (2024). https://doi.org/10.1007/s00500-023-09007-w

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