IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v10y2025i3p37-d1613980.html
   My bibliography  Save this article

Experimental Parametric Forecast of Solar Energy over Time: Sample Data Descriptor

Author

Listed:
  • Fernando Venâncio Mucomole

    (CS-OGET—Center of Excellence of Studies in Oil and Gas Engineering and Technology, Faculty of Engineering, Eduardo Mondlane University, Mozambique Avenue Km 1.5, Maputo 257, Mozambique
    CPE—Centre of Research in Energies, Faculty of Sciences, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique
    Department of Physics, Faculty of Sciences, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique)

  • Carlos Augusto Santos Silva

    (Department of Mechanical Engineering, Instituto Superior Técnico, University of Lisbon, 1600-214 Lisbon, Portugal)

  • Lourenço Lázaro Magaia

    (Department of Mathematics and Informatics, Faculty of Science, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique)

Abstract

Variations in solar energy when it reaches the Earth impact the production of photovoltaic (PV) solar plants and, in turn, the dynamics of clean energy expansion. This incentivizes the objective of experimentally forecasting solar energy by parametric models, the results of which are then refined by machine learning methods (MLMs). To estimate solar energy, parametric models consider all atmospheric, climatic, geographic, and spatiotemporal factors that influence decreases in solar energy. In this study, data on ozone, evenly mixed gases, water vapor, aerosols, and solar radiation were gathered throughout the year in the mid-north area of Mozambique. The results show that the calculated solar energy was close to the theoretical solar energy under a clear sky. When paired with MLMs, the clear-sky index had a correlational order of 0.98, with most full-sun days having intermediate and clear-sky types. This suggests the potential of this area for PV use, with high correlation and regression coefficients in the range of 0.86 and 0.89 and a measurement error in the range of 0.25. We conclude that evenly mixed gases and the ozone layer have considerable influence on transmittance. However, the parametrically forecasted solar energy is close to the energy forecasted by the theoretical model. By adjusting the local characteristics, the model can be used in diverse contexts to increase PV plants’ electrical power output efficiency.

Suggested Citation

  • Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Experimental Parametric Forecast of Solar Energy over Time: Sample Data Descriptor," Data, MDPI, vol. 10(3), pages 1-15, March.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:3:p:37-:d:1613980
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/10/3/37/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/10/3/37/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    2. Stevović, Ivan & Mirjanić, Dragoljub & Stevović, Svetlana, 2019. "Possibilities for wider investment in solar energy implementation," Energy, Elsevier, vol. 180(C), pages 495-510.
    3. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko & Hesham S. Rabayah & Raed M. Abendeh & Rami Alawneh, 2023. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations," Energies, MDPI, vol. 16(13), pages 1-24, June.
    4. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2024. "Regressive and Spatio-Temporal Accessibility of Variability in Solar Energy on a Short Scale Measurement in the Southern and Mid Region of Mozambique," Energies, MDPI, vol. 17(11), pages 1-29, May.
    5. Voyant, Cyril & Notton, Gilles & Duchaud, Jean-Laurent & Gutiérrez, Luis Antonio García & Bright, Jamie M. & Yang, Dazhi, 2022. "Benchmarks for solar radiation time series forecasting," Renewable Energy, Elsevier, vol. 191(C), pages 747-762.
    6. Benghanem, M. & Joraid, A.A., 2007. "A multiple correlation between different solar parameters in Medina, Saudi Arabia," Renewable Energy, Elsevier, vol. 32(14), pages 2424-2435.
    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. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Modeling Parametric Forecasts of Solar Energy over Time in the Mid-North Area of Mozambique," Energies, MDPI, vol. 18(6), pages 1-50, March.
    2. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review," Energies, MDPI, vol. 18(6), pages 1-51, March.
    3. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    4. El-Sebaii, A.A. & Al-Hazmi, F.S. & Al-Ghamdi, A.A. & Yaghmour, S.J., 2010. "Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia," Applied Energy, Elsevier, vol. 87(2), pages 568-576, February.
    5. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    6. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
    7. Ayub, Yousaf & Ren, Jingzheng & Shi, Tao & Shen, Weifeng & He, Chang, 2023. "Poultry litter valorization: Development and optimization of an electro-chemical and thermal tri-generation process using an extreme gradient boosting algorithm," Energy, Elsevier, vol. 263(PC).
    8. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    9. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.
    11. Peng Hao & Jun-Peng Guo & Eoghan O’Neill & Yong-Heng Shi, 2023. "When Will First-Price Work Well? The Impact of Anti-Corruption Rules on Photovoltaic Power Generation Procurement Auctions," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    12. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    13. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    14. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
    15. Benghanem, M., 2011. "Optimization of tilt angle for solar panel: Case study for Madinah, Saudi Arabia," Applied Energy, Elsevier, vol. 88(4), pages 1427-1433, April.
    16. Kamil Szostek & Damian Mazur & Grzegorz Drałus & Jacek Kusznier, 2024. "Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production," Energies, MDPI, vol. 17(19), pages 1-18, September.
    17. Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
    18. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
    19. Yagli, Gokhan Mert & Yang, Dazhi & Gandhi, Oktoviano & Srinivasan, Dipti, 2020. "Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?," Applied Energy, Elsevier, vol. 259(C).
    20. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.

    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:jdataj:v:10:y:2025:i:3:p:37-:d:1613980. 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.