Accurate prediction of photovoltaic (PV) power output is crucial for assessing the feasibility of early-stage projects in relation to specific site weather conditions. While various mathematical models have been used in the past for PV power prediction, most of them only consider irradiance and ambient temperature, neglecting other important meteorological parameters. In this article, a 1-year dataset from a high-precision meteorological station at the Green Energy Research facility is utilized, along with electrical parameters from a polycrystalline silicon c-Si PV module exposed during the study period, to forecast PV power. In addition, the accuracy of using satellite data for PV power forecasting is investigated, considering the growing trend of its utilization in recent research. Regression techniques, such as linear regression with interaction, tree regression, Gaussian process regression, ensemble learning for regression, response surface methodology, SVM cubic, and artificial neural network (ANN), are employed for PV power prediction, using both ground measurement data and satellite data. Comparatively lower accuracies are observed when using satellite data across all regression methods, in contrast to the higher accuracies achieved with ground-based measurements. Notably, the Gaussian process regression method demonstrates high accuracy ( R ² = 0.25 for satellite data and R ² = 0.94 for ground-based data). Furthermore, the ANN approach further enhances the accuracy of PV power forecasting, yielding R ² = 0.42 for satellite data and R ² = 0.96 for ground-based data. These findings emphasize the need for caution when relying on satellite data for PV power forecasting, even when employing advanced ANN approaches. It underscores the importance of considering ground-based measurements for more reliable and accurate predictions.

Photovoltaic (PV) production is highly dependent on global solar irradiance (GHI) and often experiences irregular fluctuations. With the continuous increase in PV penetration rates, GHI forecasting methods are becoming important to ensure optimal management of the energy produced. In this study, three forecasting models – Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Seasonal Autoregressive Integrated Moving Average (SARIMA) were evaluated to predict GHI one day ahead. Ground measurement data from four sites in Morocco and weather forecasts from the Global Forecast System (GFS) were used to perform the forecast. Results show that forecasts based on MLP and LSTM are more accurate than SARIMA and persistence even if under complicated weather conditions. For clear days with low variability, the RMSE for LSTM, MLP M1, and MLP M2 are 18.58, 12.97, and 45.20 W/m². For cloudy days with high variability, the RMSE are 103, 60.80 W/m², and 89.17 W/m², respectively.

The global shift toward sustainable energy systems has led to an increased adoption of PV systems, driven by their enhanced performance and environmental benefits, including reduced carbon emissions. Improving the efficiency of Grid-Tied Photovoltaic Systems (GTPVS) is essential for guaranteeing reliable and sustainable renewable power integration. This research paper presents advanced hybrid Maximum Power Point Tracking (MPPT) designed for GTPVS to maximize PV energy harvesting and support grid sustainability. The proposed technique combines Advanced Variable Step Size Incremental Conductance (AVIC) for reference voltage generation and an Integral Backstepping Control (IBC) to regulate the control of the step-up converter. This hybrid technique enables rapid convergence speed, reduces power losses, and enhances stability under fast-changing environmental conditions, Partial Shading Conditions (PSCs), and grid disturbances conditions. This MPPT is evaluated via the MATLAB/Simulink environment, version 2020b, and validated in real time using a Processor-in-the-Loop (PIL) setup on the eZdsp TMS320F28335 platform. Comparative analysis with benchmark methods confirms its superiority, with an average tracking performance of 99.57%, a response time of 0.02 s, and a Total Harmonic Distortion (THD) of 0.69%, accompanied by negligible steady-state oscillations. These findings indicate the validity and sustainability of the AVIC-IBC MPPT for real-time GTPVS operating under realistic climatic conditions.

Solar resource assessment by clear sky models is of great importance in the solar energy field: verifying the performance of photovoltaic systems during stable conditions, clouds effects evaluation, the determination of geographical areas where irradiation is more uncertain and the preparation of forecasts with sky cameras. But before using these models they must be validated against high performances soil measurements. Since there is no radiometric sensor that measures clear-sky radiation, then historical clear-sky time periods must be identified only from long-term allsky irradiation records. The contribution of this study is to exploit the ground measurements, analyze them and retrieve the information they contain concerning the clear sky instants. The study will be performed by comparing the clear sky instants identified by an algorithm proposed by Reno and Hansen with a physical clear sky model. This comparison is made using high frequency global horizontal irradiation (GHI) data from high performances meteorological station installed at Benguerir in Morocco.