The prediction of solar potential is an important step toward the evaluation of PV plant production for the best energy planning. In this study, the discrete Kalman filter model was implemented for short-term solar resource forecasting one the Dakar site in Senegal. The model input parameters are constituted at a time t of the air temperature, the relative humidity and the global solar radiation. The expected output at time t+T is the global solar radiation. The model performance is evaluated with the square root of the normalized mean squared error (NRMSE), the absolute mean of the normalized error (NMAE), the average bias error (NMBE). The model Validation is carried out by means of the data measured within the Polytechnic Higher School of Dakar for one year. The simulation results following the 20 minute horizon show a good correlation between the prediction and the measurement with an NRMSE of 4.8%, an NMAE of 0.27% and an NMBE of 0.04%. This model could contribute to help photovoltaic based energy providers to better plan the production of solar photovoltaic plants in Sahelian environments.

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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.

This study proposes a short term forecasting of solar irradiation with multi horizons in the north-west of Senegal. The multilayer artificial neural network (ANN), based on the Levenberg Marquardt algorithm and the meteorological data are used. The latter are measured in real time on the study site. The variables of interest are: mean solar irradiation, maximum temperature and measurement time; they are selected using Weka software. The forecasting horizons are: 0.5 hour, 1 hour, 1.5 hours, 2 hours, 2.5 hours, 3 hours, 3.5 hours, 4 hours, 4.5 hours, 5 hours, 5.5 hours and 06 hours. They are proposed with the corresponding statistical criteria. The results show that, the solar energy forecasting can be extended over a six-hour horizon with a correlation coefficient of 0.97 and root mean square error of 0.07. These results will make it possible to complete the forecasting tools in the solar energy sector in Senegal, and help investors to choose the most suitable horizons for energy forecasting in photovoltaic solar power plants.