IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i3p1075-d1579123.html

Prediction of Short-Term Solar Irradiance Using the ProbSparse Attention Mechanism for a Sustainable Energy Development Strategy

Author

Listed:
  • Zhenyuan Zhuang

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Huaizhi Wang

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Cilong Yu

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

Abstract

Sustainability refers to a development approach that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. Solar energy is an inexhaustible and renewable resource. From the perspective of resource utilization, solar power generation has a high degree of sustainability. Therefore, solar power generation is one of the most important ways to transform the energy structure and promote the sustainable development of the economy and society, and it is of great significance for promoting the construction of a resource-conserving and environmentally friendly society. However, solar energy resources also exhibit strong unpredictability; therefore, this paper proposes a novel artificial intelligence (AI) model for short-term solar irradiance prediction in photovoltaic power generation. Leveraging the ProbSparse attention mechanism within an encoder-decoder architecture, the AI model efficiently captures both short- and long-term dependencies in the input sequence. The dingo algorithm is innovatively redesigned to optimize the hyperparameters of the proposed AI model, enhancing model convergence. Data preprocessing involves feature selection based on mutual information, multiple imputations for data cleaning, and median filtering. Evaluation metrics include the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ). The proposed AI model demonstrates improved efficiency and robust performance in solar irradiance prediction, contributing to advancements in energy management for electrical power and energy systems.

Suggested Citation

  • Zhenyuan Zhuang & Huaizhi Wang & Cilong Yu, 2025. "Prediction of Short-Term Solar Irradiance Using the ProbSparse Attention Mechanism for a Sustainable Energy Development Strategy," Sustainability, MDPI, vol. 17(3), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1075-:d:1579123
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/3/1075/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/3/1075/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ramadhan, Raden A.A. & Heatubun, Yosca R.J. & Tan, Sek F. & Lee, Hyun-Jin, 2021. "Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power," Renewable Energy, Elsevier, vol. 178(C), pages 1006-1019.
    2. Fjelkestam Frederiksen, Cornelia A. & Cai, Zuansi, 2022. "Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance," Applied Energy, Elsevier, vol. 306(PB).
    3. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
    4. Li, Yanting & Su, Yan & Shu, Lianjie, 2014. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, Elsevier, vol. 66(C), pages 78-89.
    5. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    6. Cabello-López, Tomás & Carranza-García, Manuel & Riquelme, José C. & García-Gutiérrez, Jorge, 2023. "Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level," Applied Energy, Elsevier, vol. 350(C).
    7. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    8. Chen, Shanlin & Li, Chengxi & Xie, Yuying & Li, Mengying, 2023. "Global and direct solar irradiance estimation using deep learning and selected spectral satellite images," Applied Energy, Elsevier, vol. 352(C).
    9. Liu, Wei-Jen & Chiu, Wei-Yu & Hua, Weiqi, 2024. "Blockchain-enabled renewable energy certificate trading: A secure and privacy-preserving approach," Energy, Elsevier, vol. 290(C).
    10. Chen, Yunxiao & Bai, Mingliang & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2023. "Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting," Energy, Elsevier, vol. 284(C).
    11. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
    12. Zhang, Kai & Bai, Dongxin & Li, Yong & Song, Ke & Zheng, Bailin & Yang, Fuqian, 2024. "Robust state-of-charge estimator for lithium-ion batteries enabled by a physics-driven dual-stage attention mechanism," Applied Energy, Elsevier, vol. 359(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konduru Sudharshan & C. Naveen & Pradeep Vishnuram & Damodhara Venkata Siva Krishna Rao Kasagani & Benedetto Nastasi, 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction," Energies, MDPI, vol. 15(17), pages 1-39, August.
    2. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).
    3. Li, Xuemei & Li, Jiakai & Zhao, Yufeng & Zhou, Shiwei, 2025. "A novel discrete multivariable grey model with seasonal time-lag effect for clean energy generation forecasting," Energy, Elsevier, vol. 334(C).
    4. Yang, Wendong & Zang, Xinyi & Wu, Chunying & Hao, Yan, 2024. "A new multi-objective ensemble wind speed forecasting system: Mixed-frequency interval-valued modeling paradigm," Energy, Elsevier, vol. 304(C).
    5. Amini Toosi, Hashem & Del Pero, Claudio & Leonforte, Fabrizio & Lavagna, Monica & Aste, Niccolò, 2023. "Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization," Applied Energy, Elsevier, vol. 334(C).
    6. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(C).
    7. Li, Peidu & Luo, Yong & Xia, Xin & Gao, Xiaoqing & Chang, Rui & Li, Zhenchao & Zheng, Junqing & Shi, Wen & Liao, Zhouyi, 2024. "Factors and quantitative impact on electrical yield in fishery complementary photovoltaic power plant under different cloud cover conditions," Energy, Elsevier, vol. 309(C).
    8. Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    9. Xiao, Liexi & Meng, Anbo & Rong, Jiayu & Zhang, Haitao & Xian, Zikang & Li, Chen & Zhu, Jianbin & Yin, Yiding & Li, Hanhong & Tang, Yanshu & Yin, Hao & Li, Xuecong & Liu, Jiawei, 2025. "A dual-dimensionality reduction attention mechanism with fusion of high-dimensional features for wind power prediction," Renewable Energy, Elsevier, vol. 247(C).
    10. Abdallah Abdellatif & Hamza Mubarak & Shameem Ahmad & Tofael Ahmed & G. M. Shafiullah & Ahmad Hammoudeh & Hamdan Abdellatef & M. M. Rahman & Hassan Muwafaq Gheni, 2022. "Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
    11. Min, Hyunsik & Noh, Byeongjoon, 2025. "SolarNexus: A deep learning framework for adaptive photovoltaic power generation forecasting and scalable management," Applied Energy, Elsevier, vol. 391(C).
    12. Jibo Wang & Zihao Zhang & Wenhao Xu & Yijin Li & Geng Niu, 2025. "Short-Term Photovoltaic Power Forecasting Using a Bi-LSTM Neural Network Optimized by Hybrid Algorithms," Sustainability, MDPI, vol. 17(12), pages 1-22, June.
    13. Zhang, Qiongfang & Yan, Hao & Liu, Yongming, 2024. "Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    14. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
    15. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    16. Chen, Huize & Jiang, Xiaomo & Hui, Huaiyu & Zhang, Kexin & Meng, Wenqing & Cheynet, Etienne, 2026. "Enhancing probabilistic wind speed forecasting by integrating self-adaptive Bayesian wavelet denoising with deep Gaussian process regression under uncertainties," Renewable Energy, Elsevier, vol. 256(PB).
    17. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    18. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    19. Zhang, Zongbin & Huang, Xiaoqiao & Li, Chengli & Cheng, Feiyan & Tai, Yonghang, 2025. "CRAformer: A cross-residual attention transformer for solar irradiation multistep forecasting," Energy, Elsevier, vol. 320(C).
    20. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1075-:d:1579123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.