Mapping of the Literal Regressive and Geospatial–Temporal Distribution of Solar Energy on a Short-Scale Measurement in Mozambique Using Machine Learning Techniques

The earth’s surface has an uneven solar energy density that is sufficient to stimulate solar photovoltaic (PV) production. This causes variations in a solar plant’s output, which are impacted by geometrical elements and atmospheric conditions that prevent it from passing. Motivated by the focus on encouraging increased PV production efficiency, the goal was to use machine learning models (MLM) to map the distribution of solar energy in Mozambique in a regressive literal and geospatial–temporal manner on a short measurement scale. The clear-sky index K t * theoretical approach was applied in conjunction with MLM that emphasized random forest (RF) and artificial neural networks (ANNs). Solar energy mapping was the result of the methodology, which involved statistically calculating K t * for the analysis of solar energy in correlational and causal terms of the space-time distribution. Utilizing data from PVGIS, NOAA, NASA, and Meteonorm, a sample of solar energy was gathered at 11 measurement stations in Mozambique over a period of 1 to 10 min between 2012 and 2014 as part of the FUNAE and INAM measurement programs. The statistical findings show a high degree of solar energy incidence, with increments ∆ K t * in the average order of −0.05 and K t * mostly ranging between 0.4 and 0.9. In 2012 and 2014, K t * was 0.8956 and 0.6986, respectively, because clear days had a higher incident flux and intermediate days have a higher frequency of ∆ K t * on clear days and a higher occurrence density. There are more cloudy days now 0.5214 as opposed to 0.3569. Clear days are found to be influenced by atmospheric transmittance because of their high incident flux, whereas intermediate days exhibit significant variations in the region’s solar energy.