An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power

Ran, Minghao; Wang, Yingchao; Qin, Qilu; Huang, Jindi; Jiang, Jiading

doi:10.3390/su17115009

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An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power

by

Minghao Ran

^1,2

,

Yingchao Wang

^1,*

,

Qilu Qin

²,

Jindi Huang

² and

Jiading Jiang

¹

School of Energy Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China

²

School of Business, Jiangnan University, Wuxi 214122, China

^*

Author to whom correspondence should be addressed.

Sustainability 2025, 17(11), 5009; https://doi.org/10.3390/su17115009

Submission received: 4 April 2025 / Revised: 27 May 2025 / Accepted: 27 May 2025 / Published: 29 May 2025

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Abstract

Due to the prevalent “small data”, “seasonal”, and “periodicity” characteristics in China’s renewable energy power generation data, there are certain difficulties in long-term power generation prediction. For this reason, this paper uses the data preprocessing method of periodical aggregation to enhance the “quasi-exponentiality” characteristics of original data, eliminate “seasonality” and “periodicity”, use the DGM(1,1) model to predict aggregated data, and then use the periodical component factor to reduce the DGM(1,1)-predicted data. A seasonal discrete grey prediction model based on periodical aggregation is constructed. The proposed methodology employs streamlined data preprocessing coupled with conventional grey prediction modeling to enable the precise forecasting of nonlinear periodic sequences. This approach demonstrates an enhanced operational efficiency by mitigating the structural complexity and implementation barriers inherent in classical seasonal grey prediction frameworks. Validation experiments conducted on China’s photovoltaic (PV) and wind power generation datasets through comparative multi-model analysis confirm the model’s superior predictive accuracy, with performance metrics significantly outperforming benchmark methods across both training and validation cohorts.

Keywords: periodical aggregation; data preprocessing; grey prediction; periodical seasonality; renewable energy

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MDPI and ACS Style

Ran, M.; Wang, Y.; Qin, Q.; Huang, J.; Jiang, J. An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power. Sustainability 2025, 17, 5009. https://doi.org/10.3390/su17115009

AMA Style

Ran M, Wang Y, Qin Q, Huang J, Jiang J. An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power. Sustainability. 2025; 17(11):5009. https://doi.org/10.3390/su17115009

Chicago/Turabian Style

Ran, Minghao, Yingchao Wang, Qilu Qin, Jindi Huang, and Jiading Jiang. 2025. "An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power" Sustainability 17, no. 11: 5009. https://doi.org/10.3390/su17115009

APA Style

Ran, M., Wang, Y., Qin, Q., Huang, J., & Jiang, J. (2025). An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power. Sustainability, 17(11), 5009. https://doi.org/10.3390/su17115009

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An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power

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