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

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  • 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
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    References listed on IDEAS

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    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.
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    1. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Mapping of the Literal Regressive and Geospatial–Temporal Distribution of Solar Energy on a Short-Scale Measurement in Mozambique Using Machine Learning Techniques," Energies, MDPI, vol. 18(13), pages 1-55, June.
    2. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Digital Accessibility of Solar Energy Variability Through Short-Term Measurements: Data Descriptor," Data, MDPI, vol. 10(10), pages 1-18, September.

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