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.

This study was carried out at the International Center for Training and Research in Solar Energy(CIFRES) and its main purpose was to study the performance of a solar module cleaning system. To handle this work, a measuring platform consisting of two polycrystalline (pc-Si) PV modules was designed. The modules were connected to a waterless cleaning system on the surface of the solar panels. The platform also contained a temperature sensor on the surface of the module, a pyranometer, shunt resistors (for current measurement), and an acquisition unit. This platform was exposed under real conditions and measurements of the parameters were taken in increments of ten seconds. Only one of the two modules was cleaned daily, and an evaluation of the degradation rate of the short-circuit current (I sc ) of the dust module with respect to the cleaned module was carried out. After one month of exposure, the analysis of the results showed a degradation rate of 17.13% of the short circuit current (I sc ) of the dirty module compared to the clean module. Compared to the initial conditions under the standard test conditions, a degradation of 10.16 and 24.09%, respectively for the clean module and the dirty module was obtained. This work also showed that a polynomial relation exists between the degradation rate and the dust deposition density with a coefficient of determination of 0.9933.

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The accumulation of dust on the surface of solar panels reduces the amount of sunlight reaching the solar cells and results in a decrease in panel performance. To avoid this loss of production and thus, to improve the performance capacity, solar panels must be cleaned frequently. The West African region is well known for its high solar energy potential. However, this potential can be reduced by the high occurrence of dust storms during the year. This article aims to provide a contribution to the construction of a meteorological information service for solar panel cleaning operations at Diass solar plant in Senegal (Western Sahel). It is based on a full year in situ experiment comparing the power loss due to dust between solar panels cleaned at different frequencies and those not cleaned. The model to determine the cleaning frequencies is based on the deposition rate of airborne particles, the concentration of airborne particles, and the density of the dust that has a major impact on the power loss. Cleaning frequencies are presented at seasonal scale because in the study area, dust episodes differ according to the seasons. A cost–benefit analysis is also performed to demonstrate the advantage of using weather information service to support the dust cleaning operations at Diass plant. As results, it is found that cleaning every 3 weeks is required during the dry seasons, December–January–February and March–April–May. During the rainy season, cleaning every 5 weeks is recommended in June–July–August, while in September–October–November cleaning every 4 weeks is sufficient to maintain an optimal performance of the solar panel. The total costs of cleaning operations based on these results are reduced compared to the current costs of cleaning and the benefits are much higher than without cleaning action.