Hamza Mabchour

Short-term wind speed forecasting is essential for maintaining grid stability and supporting the integration of renewable energy, yet the strong variability of wind makes accurate prediction difficult. Sudden fluctuations and nonlinear atmospheric behavior often reduce the performance of conventional artificial intelligent models. To address this challenge, this study evaluates three forecasting methods which include a gated recurrent unit (GRU) model, a temporal convolutional networks (TCNs) model, and a hybrid GRU–TCN design that enables prolonged term forecasting while enabling quick identification of localized weather changes across various meteorological parameters. The researchers used Laayoune, Morocco data to build their model training process. The hybrid method exceeded all other models because it achieved an R² value of 0.99 and a root mean square error (RMSE) of 0.16 m/s and a mean absolute error (MAE) of 0.03 m/s. The system successfully manages sudden shifts in wind patterns while maintaining accurate site-specific physical behavior. The hybrid GRU–TCN design functions as a dependable and expandable system, which delivers real-time wind forecasting capabilities that enable effective smart grid operations and facilitate the growth of wind energy systems.

The increasing penetration of photovoltaic (PV) systems in smart grids highlights the need for reliable solutions to mitigate the inherent intermittency of solar energy. Short-term variability in solar irradiance remains a critical challenge for stable grid operation and efficient PV energy management. This paper proposes an integrated forecasting-control framework that combines short-term global horizontal irradiance (GHI) prediction with a conventional P&O MPPT strategy to enhance PV system performance. A hybrid LSTM-CNN architecture is developed to forecast one-step-ahead GHI under the semi-arid climatic conditions of Dakhla, Morocco, a region characterized by high solar potential and pronounced irradiance fluctuations. The forecasting model is validated using measured irradiance data from the National Renewable Energy Laboratory (NREL) via the National Solar Radiation Database (NSRDB). Predicted irradiance is then used to improve PV power estimation and support predictive maximum power point tracking (MPPT) operation. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed framework achieves accurate GHI forecasting, faster MPPT convergence, reduced steady-state oscillations, and improved PV power stability under rapidly changing irradiance. The proposed approach provides a practical and computationally efficient solution for enhancing the dynamic response and energy extraction efficiency of PV systems in smart grid applications.

Hybrid renewable energy systems (HRES) present a promising solution for improving energy reliability and reducing costs in remote, off-grid areas. This study explores the feasibility of implementing an HRES in Dakhla, Morocco, where conventional electrical infrastructure is lacking. By integrating photovoltaic (PV) panels, wind turbines, diesel generators, and battery storage, this study aims to optimize energy resource management while balancing technical performance and economic viability. Using real- world data on energy consumption, climatic conditions, and installation constraints, advanced simulation tools such as HOMER were employed to evaluate both technical and economic parameters. The objective was to minimize the cost of energy (COE) while ensuring reliability, availability, and a high renewable fraction. The results, compared with optimization algorithms like genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), revealed the PV-wind-diesel-battery configuration as the most cost-effective solution. This configuration resulted in a net present cost (NPC) of $829,380, a COE of $0.160/kWh, and minimal CO2 emissions of 54.9 kg/year. The findings highlight the viability of this hybrid microgrid as a sustainable off-grid electrification solution and emphasize the role of renewable energy in addressing global energy challenges.

In the present work, we consider magneto-conductivity behavior within a two-dimensional p-Si/SiGe/Si system. A theoretical model of magneto-conductivity with three important contributions is derived, namely, an account for the weak localization process, for electron-electron interaction effects, and for the Zeeman effect, which is taken into account only if a magnetic field is present. Further, theoretical values predicted by our model are compared with experimental magneto-conductivity values for a comparison of similarity and difference with experimental values. In our research, experimental data for p-Si/SiGe/Si were used, which has been previously collected by I. L. Drichko et al. [1] for supporting purposes.

Wind energy is a critical component of the global shift to renewable energy sources, with significant growth driven by the need to reduce carbon emissions. Accurate wind speed prediction is crucial for increasing wind energy output since it directly influences wind farm design and performance. The purpose of this study is to compare two artificial neural network (ANN) models for predicting wind speed in Dakhla City, a place with a high wind energy potential. The first model is a multilayer perceptron (MLP) trained with the backpropagation algorithm, while the second is a nonlinear autoregressive with exogenous inputs (NARX) model, a form of recurrent neural network (RNN) noted for its ability to handle time-series data more well. The comparative analysis results show that the NARX model outperforms the MLP model in terms of wind speed forecast accuracy. The NARX model achieved a near-perfect regression coefficient (R) of 0.9998 and a root mean square error (RMSE) of 1.02899, indicating that it can represent complex, nonlinear wind speed patterns. These findings indicate that the NARX model could be a beneficial tool for increasing the efficiency of Dakhla City’s wind energy infrastructure, assisting the region in meeting its renewable energy ambitions.

In this study, we investigate the magnetoconductivity behavior in a 2D p-Si/SiGe/Si system. To achieve this, we develop a theoretical model that incorporates three key contributions, the weak localization effect, electron–electron interaction effects, and the Zeeman effect, which is considered only in the presence of a magnetic field. We then compare our theoretical predictions with experimental magnetoconductivity data, analyzing both the consistencies and discrepancies between the model and the measurements. Through this comparison, we aim to provide a deeper physical understanding of the factors influencing magnetoconductivity in this system.

Design monopole antenna of ultra-wideband high bandwidth and high efficiency for ground penetrating radar application

The Density-Functional Theory (DFT) is a reformulation of the quantum study of a correlated N-body system into a simpler system with independent equations being solved iteratively. The DFT considers only ground states of the systems. The extension to the time-dependent case of this theory is the Time-Dependent Density-Functional Theory (TDDFT) that also takes into account the excited states of the system. These calculations are very interesting in photonics areas. In fact, the interaction between electrons and light in the vicinity of solid surfaces and nanostructures is important as pathway to integrate photonics and electronics. The capability to couple light and electrons in purposefully designed device depends on the capability of creating such devices and the understanding of the underlying science.