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The knowledge of the climatic parameters' evolution represents a determining factor for design, dimensioning, performance assessment, and energetic management of renewable energy conversion systems. In particular, the solar energy conversion systems are essentially sensitive to sunlight and ambient temperature. However, for the efficient functioning and better performance of renewable energy systems, the information of solar radiation and its components at particular location in the ground is very essential. But harvesting this energy efficiently is a huge challenge. In developing countries like ours, the number of observing stations is inadequate and direct measuring is not always available for various reasons. Therefore, the need for empirical relations becomes effective alternatives to estimate global solar radiation for the places where measurements are not carried out and for the places where measurement records are not available. In this paper, four empirical models are evaluated across four localities in Cameroon and Senegal and compared with the solar radiation values measured by the meteorological stations or obtained from different databases. The studied models are those of Hargreaves and Samani [J. Irrig. Drain. Eng., Am. Soc. Civ. Eng. 108(3), 225–230 (1982)], Annandale et al. [Irrig. Sci. 21, 57–67 (2002)], Bristow and Campbell [Agric. For. Meteorol. 31, 159–166 (1984)], and Goodin et al. [Agron. J. 91, 845–851 (1999)]. According to the simulation results, the mean yearly radiation received are 3.8414 kWh m−2 d−1 for Yaounde; 3.9869 kWh m−2 d−1 for Garoua; 2.2539 kWh m−2 d−1 for Dakar; and 5.9159 kWh m−2 d−1 for Gandon. Of all the models evaluated in this study, those proposed by Annandale et al. [Irrig. Sci. 21, 57–67 (2002)] and Goodin et al. [Agron. J. 91, 845–851 (1999)] produced estimates that are statistically significant at the particular confidence level, i.e., 1 − α = 99%, as the calculated t values are less than the critical t value (2.5758). Taking into account the foregoing, we can state that these models are suitable in estimating solar radiation in the localities where only air temperature data are available.

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In this paper, artificial neural networks (ANNs) have been used for the performance ratio modelling of four photovoltaic (PV) modules. The PV modules are selected from three different silicon technologies including one monocrystalline, two polycrystalline, and one micromorph (a-Si/μc-Si) modules. The adopted ANN architecture is a multilayer perceptron (MLP). The inputs of the ANN models are the solar irradiance on the PV module plane and air ambient temperature, while the output is the PV module performance ratio. It is shown that ANN models with three layers and five hidden neurons accurately model the performance ratio regardless of PV module technology. The results obtained from the ANN model are compared with those obtained from the five parameter model (L5P). The model comparison is done through two widely used forecasting errors: the root mean square error (RMSE) and the mean absolute percentage of error (MAPE). The values of both RMSE and MAPE are less than 0.02 for MLP based models and are about three to nine times lower than those obtained from the electrical model. It is also shown that the poor fit of the L5P model is due to the bad estimation of series and shunt resistances.