Developing and using solar energy has become an important strategic decision for sustainable development in many countries. Short-term changes in solar irradiance can affect the safety and stability of photovoltaic and solar thermal power plants, so the accuracy of solar irradiance prediction has attracted significant attention. This paper proposes a short-term irradiance prediction method based on an improved complete ensemble empirical mode decomposition with adaptive noise and the partial differential equation model. Image feature information is obtained from ground-based sky images, and two ordinary differential equation (ODE) networks are used to process historical irradiance information and exogenous variables, including historical meteorological and sky images information. Using the ODE solver, the temporal pattern of the target sequence and the serial correlation between the exogenous variables are obtained, and an irradiance prediction model based on multivariate time series is established. The proposed method is evaluated using a public dataset from California, USA, and locally collected datasets. The experimental results show that the proposed method has high prediction accuracy and significantly improves the estimation of solar irradiance.
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March 2025
Research Article|
March 20 2025
Ultra-short-term prediction of solar irradiance with multiple exogenous variables by fusion of ground-based sky images

Xiaopeng Sun
;
Xiaopeng Sun
(Conceptualization, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
College of Electrical and Power Engineering, Taiyuan University of Technology
, Taiyuan 030024, China
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Wenjie Zhang
;
Wenjie Zhang
a)
(Formal analysis, Investigation, Supervision, Writing – review & editing)
1
College of Electrical and Power Engineering, Taiyuan University of Technology
, Taiyuan 030024, China
a)Author to whom correspondence should be addressed: [email protected]
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Mifeng Ren;
Mifeng Ren
(Formal analysis, Investigation, Writing – review & editing)
1
College of Electrical and Power Engineering, Taiyuan University of Technology
, Taiyuan 030024, China
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Zhujun Zhu
;
Zhujun Zhu
(Formal analysis, Investigation, Project administration, Resources)
2
Shanxi Gemeng US-China Clean Energy R&D Center Co., Ltd.
, Taiyuan 030031, China
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Gaowei Yan
Gaowei Yan
(Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft)
1
College of Electrical and Power Engineering, Taiyuan University of Technology
, Taiyuan 030024, China
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Xiaopeng Sun
1
Wenjie Zhang
1,a)
Mifeng Ren
1
Zhujun Zhu
2
Gaowei Yan
1
1
College of Electrical and Power Engineering, Taiyuan University of Technology
, Taiyuan 030024, China
2
Shanxi Gemeng US-China Clean Energy R&D Center Co., Ltd.
, Taiyuan 030031, China
a)Author to whom correspondence should be addressed: [email protected]
J. Renewable Sustainable Energy 17, 023501 (2025)
Article history
Received:
November 15 2024
Accepted:
February 25 2025
Connected Content
A companion article has been published:
Employing sky images for ultra-short-term solar forecasts
Citation
Xiaopeng Sun, Wenjie Zhang, Mifeng Ren, Zhujun Zhu, Gaowei Yan; Ultra-short-term prediction of solar irradiance with multiple exogenous variables by fusion of ground-based sky images. J. Renewable Sustainable Energy 1 March 2025; 17 (2): 023501. https://doi.org/10.1063/5.0249194
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