{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T03:12:10Z","timestamp":1772593930934,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CS\u2013OGET, Faculty of Engineering, Eduardo Mondlane University","award":["CS-OGET\/2023"],"award-info":[{"award-number":["CS-OGET\/2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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\u2019 electrical power output efficiency.<\/jats:p>","DOI":"10.3390\/data10030037","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T07:49:57Z","timestamp":1742197797000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Experimental Parametric Forecast of Solar Energy over Time: Sample Data Descriptor"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9534-1160","authenticated-orcid":false,"given":"Fernando Ven\u00e2ncio","family":"Mucomole","sequence":"first","affiliation":[{"name":"CS-OGET\u2014Center 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"},{"name":"CPE\u2014Centre of Research in Energies, Faculty of Sciences, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique"},{"name":"Department of Physics, Faculty of Sciences, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-3993","authenticated-orcid":false,"given":"Carlos Augusto Santos","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Instituto Superior T\u00e9cnico, University of Lisbon, 1600-214 Lisbon, Portugal"}]},{"given":"Louren\u00e7o L\u00e1zaro","family":"Magaia","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Informatics, Faculty of Science, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"ref_1","unstructured":"Wenham, S.R., Green, M.A., Watt, M.E., Corkish, R., and Sproul, A. 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