IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v237y2024ipas0960148124017002.html
   My bibliography  Save this article

Development of an engineering-friendly evaluation model for solar spectral irradiance using readily accessible subaerial meteorology

Author

Listed:
  • Wang, Shuhao
  • Peng, Jinqing
  • Luo, Yimo
  • Ma, Tao
  • Xue, Peng
  • Wu, Yupeng
  • Zhang, Qiangzhi
  • Zhou, Jiayu

Abstract

Solar spectral irradiance has a crucial impact on building energy conservation, especially on photovoltaic (PV) generation. However, it takes a high cost to measure and predict the dynamic solar spectral irradiance for various atmosphere conditions and sun positions. Combining with machine learning, this paper developed a novel solar spectral irradiance estimation model to evaluate the annual solar spectral property in a region. This paper employs the readily accessible subaerial meteorology as model input. The average photon energy (APE) serves as a connection between the normalized solar spectral irradiance and the meteorology parameters. Verification showed the model this paper proposed estimated the normalized solar spectral irradiance well. Further, annual simulation of solar spectral irradiance was conducted by inputting typical meteorology year (TMY) dataset. The annual difference of the normalized spectral irradiance reached to 10.57 %, which reflects the great importance to determine the practical solar spectral irradiance. A typical day of spectra was proposed for each month to reveal the monthly variation in solar spectral irradiance. This study provides a convenient technical method to evaluate the solar spectral property for engineering applications. The results may guide industries in selecting suitable solar cells for the region, thereby prompting the development of solar applications.

Suggested Citation

  • Wang, Shuhao & Peng, Jinqing & Luo, Yimo & Ma, Tao & Xue, Peng & Wu, Yupeng & Zhang, Qiangzhi & Zhou, Jiayu, 2024. "Development of an engineering-friendly evaluation model for solar spectral irradiance using readily accessible subaerial meteorology," Renewable Energy, Elsevier, vol. 237(PA).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pa:s0960148124017002
    DOI: 10.1016/j.renene.2024.121632
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124017002
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.121632?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    2. Wang, Shuhao & Peng, Jinqing & Wang, Meng & Xue, Peng & Luo, Yimo & Ma, Tao & Zhao, Yifan, 2023. "Evaluation of the energy conversion performance of different photovoltaic materials with measured solar spectral irradiance," Renewable Energy, Elsevier, vol. 219(P1).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sang, Wenhu & Ma, Yuxin & Li, Senyuan & Xue, Peng & Li, Bojia & Peng, Jinqing & Fan, Man, 2024. "Spectral correction of photovoltaic module electrical properties," Renewable Energy, Elsevier, vol. 237(PD).

    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. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    2. Jean-Laurent Duchaud & Cyril Voyant & Alexis Fouilloy & Gilles Notton & Marie-Laure Nivet, 2020. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control," Energies, MDPI, vol. 13(14), pages 1-16, July.
    3. Gairaa, Kacem & Voyant, Cyril & Notton, Gilles & Benkaciali, Saïd & Guermoui, Mawloud, 2022. "Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities," Renewable Energy, Elsevier, vol. 183(C), pages 890-902.
    4. Boumediene Ladjal & Imad Eddine Tibermacine & Mohcene Bechouat & Moussa Sedraoui & Christian Napoli & Abdelaziz Rabehi & Djemoui Lalmi, 2024. "Hybrid models for direct normal irradiance forecasting: a case study of Ghardaia zone (Algeria)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(15), pages 14703-14725, December.
    5. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    6. Sang, Wenhu & Ma, Yuxin & Li, Senyuan & Xue, Peng & Li, Bojia & Peng, Jinqing & Fan, Man, 2024. "Spectral correction of photovoltaic module electrical properties," Renewable Energy, Elsevier, vol. 237(PD).
    7. Ines Sansa & Zina Boussaada & Najiba Mrabet Bellaaj, 2021. "Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX," Energies, MDPI, vol. 14(21), pages 1-26, October.
    8. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    9. Sonia Montecinos & Carlos Rodríguez & Andrea Torrejón & Jorge Cortez & Marcelo Jaque, 2024. "Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks," Energies, MDPI, vol. 17(23), pages 1-17, November.
    10. Tyler McCandless & Susan Dettling & Sue Ellen Haupt, 2020. "Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction," Energies, MDPI, vol. 13(3), pages 1-14, February.
    11. Carolina Euán & Ying Sun & Brian J. Reich, 2022. "Statistical analysis of multi‐day solar irradiance using a threshold time series model," Environmetrics, John Wiley & Sons, Ltd., vol. 33(3), May.
    12. Sarah Ouédraogo & Ghjuvan Antone Faggianelli & Guillaume Pigelet & Jean Laurent Duchaud & Gilles Notton, 2020. "Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island," Energies, MDPI, vol. 13(17), pages 1-20, September.
    13. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
    14. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    15. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
    16. Yuan, Jun & Nian, Victor & He, Junliang & Yan, Wei, 2019. "Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources," Energy, Elsevier, vol. 189(C).
    17. Cecilia Martinez-Castillo & Gonzalo Astray & Juan Carlos Mejuto, 2021. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models," Energies, MDPI, vol. 14(8), pages 1-16, April.
    18. Li, Chengdong & Zhou, Changgeng & Peng, Wei & Lv, Yisheng & Luo, Xin, 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method," Energy, Elsevier, vol. 212(C).
    19. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    20. Adel Alblawi & M. H. Elkholy & M. Talaat, 2019. "ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance," Sustainability, MDPI, vol. 11(23), pages 1-24, November.

    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:eee:renene:v:237:y:2024:i:pa:s0960148124017002. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.