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Regressive and Spatio-Temporal Accessibility of Variability in Solar Energy on a Short Scale Measurement in the Southern and Mid Region of Mozambique

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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

Solar energy reaching a horizontal surface can possess fluctuations that impact electricity generation at a solar plant. Despite this, energy access remains inadequate, particularly in rural areas, with an estimated 82% deficiency. This drives us to assess the regressive and spatial-temporal accessibility of solar energy in the southern and mid regions of Mozambique. This evaluation aims to determine the actual availability of energy for electrification purposes. Data on global horizontal irradiation from approximately 8 stations across all provinces in the specified regions, collected between 2012 and 2014 at intervals of 1 and 10 min, were analyzed using regression and correlation methods along with a specialized algorithm for classifying days based on clear sky index terms. The statistical analysis identified days with significant potential for energy accessibility, exceeding 50% of the average. The findings suggest a correlation coefficient of approximately 0.30 for energy and non-linear regression with clear sky index coefficients around 0.80. The method employed demonstrated accuracy when compared to theoretical simulations of the clear sky index in the region, indicating its potential applicability in other regions of interest.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2613-:d:1404231
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    References listed on IDEAS

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    1. Takilalte, Abdelatif & Harrouni, Samia & Yaiche, Mohamed Rédha & Mora-López, Llanos, 2020. "New approach to estimate 5-min global solar irradiation data on tilted planes from horizontal measurement," Renewable Energy, Elsevier, vol. 145(C), pages 2477-2488.
    2. Williamson, Sarah & Businger, Steven & Matthews, Dax, 2018. "Development of a solar irradiance dataset for Oahu, Hawai'i," Renewable Energy, Elsevier, vol. 128(PA), pages 432-443.
    3. Armstrong, S. & Hurley, W.G., 2010. "A new methodology to optimise solar energy extraction under cloudy conditions," Renewable Energy, Elsevier, vol. 35(4), pages 780-787.
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    1. 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.
    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. 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.

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