Modeling Parametric Forecasts of Solar Energy over Time in the Mid-North Area of Mozambique

Fernando Venâncio Mucomole (), Carlos Augusto Santos Silva and Lourenço Lázaro Magaia
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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
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

Energies, 2025, vol. 18, issue 6, 1-50

Abstract: Because of variations in the amount of solar energy that reaches the Earth’s surface, the output of solar power plants can undergo significant variability in the electricity generated. To solve this conundrum, modeling the parametric forecast of short-scale solar energy across Mozambique’s Mid-North region was the goal of this study. The parametric model applied consists of machine learning models based on the parametric analysis of all atmospheric, geographic, climatic, and spatiotemporal elements that impact the fluctuation in solar energy. It highlights the essential importance of the exact management of the interferential power density of each parameter influencing the availability of super solar energy. It enhances the long and short forecasts, estimates and scales, and geographic location, and provides greater precision, compared to other forecasting models. We selected eleven Mid-North region sites that collected data between 2019 and 2021 for the validation sample. The findings demonstrate a significant connection in the range of 0.899 to 0.999 between transmittances and irradiances caused by aerosols, water vapor, evenly mixed gases, and ozone. Uniformly mixed gases exhibit minimal attenuation, with a transmittance of about 0.985 in comparison to other atmospheric constituents. Despite the increased precision obtained by parameterization, the area still offers potential for solar application, with average values of 25% and 51% for clear skies and intermediate conditions, respectively. The estimated solar energy allows the model to be evaluated in any reality since it is within the theoretical irradiation spectrum under clear skies.

Keywords: forecast; solar energy; parametric model; mid-north; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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