Mustapha Mabrouki

The reliable long-term prediction of photovoltaic (PV) system performance is essential for optimizing operation, maintenance, and energy management in smart cities. Digital Twin (DT) technology has emerged as a promising approach for real-time monitoring and predictive analytics by continuously integrating physical system measurements with virtual models. However, many existing DT-based studies rely on limited validation periods, lack comprehensive uncertainty quantification, and provide insufficient comparisons with conventional forecasting approaches. To address these limitations, this study proposes a data-driven Digital Twin framework for daily photovoltaic energy production prediction for three silicon photovoltaic technologies (amorphous silicon, polycrystalline silicon, and monocrystalline silicon) operating under semi-arid climatic conditions in Morocco. The framework integrates data quality assessment and statistical production trend analysis based on linear regression, bootstrap confidence intervals, the Mann–Kendall trend test, and Sen’s slope estimator. In addition, the proposed DT model was benchmarked against persistence, linear regression, Random Forest, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) models using a chronological training, validation, and independent testing framework. Model performance was evaluated over an independent test period of 602 consecutive days, representing approximately 1.65 years of continuous operation and more than one complete annual cycle. The proposed Digital Twin improved upon the standalone LightGBM model, achieving an RMSE of 1.3047 kWh/day, an MAE of 0.9255 kWh/day, a MAPE of 14.62%, and an R² of 0.7015, compared with an RMSE of 1.3349 kWh/day, an MAE of 0.9637 kWh/day, and an R² of 0.6875 for LightGBM. Over the 11-year monitoring period, the estimated long-term production trend rates were −0.566% yr⁻¹ for a-Si, −0.335% yr⁻¹ for pc-Si, and −0.260% yr⁻¹ for mc-Si. Bootstrap analysis yielded median long-term production trend rates of −0.573, −0.326, and −0.251% yr⁻¹, respectively, while Mann–Kendall tests indicated no statistically significant monotonic production trend for any technology (p = 0.1611, 0.2758, and 0.3502, respectively). The extended independent testing period, combined with statistical production trend analysis and comparative machine-learning evaluation, demonstrates the applicability of the proposed Digital Twin framework for photovoltaic performance monitoring and adaptive prediction under semi-arid climatic conditions.

This study investigates the long-term performance degradation and forecasting of three silicon-based photovoltaic technologies—polycrystalline (pc-Si), monocrystalline (mc-Si), and amorphous silicon (a-Si)—using a seven-year dataset (2015–2021) from a semi-arid climate. Degradation rates are quantified through seasonal-trend decomposition and Arrhenius analysis, revealing distinct mechanisms: pc-Si exhibits the lowest annual degradation (0.36%/year), followed by a-Si (0.57%/year), while mc-Si shows the highest (0.77%/year), with a notable thermal annealing effect partially compensating degradation in a-Si. For forecasting performance ratio, four models are compared, where long short-term memory networks achieve the highest accuracy by capturing nonlinear temporal dependencies, while SARIMA offers robust, interpretable results with lower complexity. Beyond predictive performance, the study establishes links between model behavior and underlying physical processes such as degradation and annealing, and analyzes prediction uncertainty in relation to temperature variability and dust accumulation. These findings highlight trade-offs between accuracy, interpretability, and deployment feasibility, providing a framework for PV performance forecasting under univariate, semi-arid conditions, with future work directed toward multivariate, physics-informed approaches across broader technologies and climates.

The journal retracts the article titled, “Bio-Aerodynamic Flow Field Optimization in PEM Fuel Cells: A Peregrine Falcon-Inspired Flow Field Approach”

To simultaneously improve mass transfer and minimize pressure drop in proton exchange membrane fuel cells (PEMFCs), this study proposes a novel bionic flow field inspired by the streamlined abdominal structure of the peregrine falcon. A three-dimensional channel geometry is developed from this biological prototype and integrated into a single-channel PEMFC model for numerical simulation. A series of computational fluid dynamics (CFD) analyses compare the new design against conventional straight, trapezoidal, and sinusoidal flow fields. The results demonstrate that the falcon-inspired configuration enhances oxygen delivery, optimizes water management, and achieves a more uniform current density distribution. Remarkably, the design delivers a 9.45% increase in peak power density while significantly reducing pressure drop compared to the straight channel. These findings confirm that biologically optimized aerodynamic structures can provide tangible benefits in PEMFC flow field design by boosting electrochemical performance and lowering parasitic losses. Beyond fuel cells, this bio-inspired approach offers a transferable methodology for advanced energy conversion systems where efficient fluid transport is essential.

Clogging, a physicochemical adhesion phenomenon, occurs between material surfaces, prompting our investigation into phosphate adhesion on steel surfaces and its associated clogging. Phosphate pellets, produced under varying pressures (80–340 bars) with 25 % water content, were analyzed after drying at 60 °C. Physicochemical interactions were explored through contact angle measurements, showing a decrease from 71° to 65° as compaction pressure increased, and surface energy calculations indicated an increase from 49 mJ/m² to 52.5 mJ/m². The phosphate originated from the extraction zone of the OCP in Ben Guerir, Morocco. Contact angle measurements on stainless steels (304, 304L, and 316) revealed that 316 steel exhibited hydrophobic behavior (contact angle 94°, surface energy 35 mJ/m²), while 304 and 304L were hydrophilic with contact angles of 68° and 70°, and surface energies of 48 mJ/m² and 45 mJ/m², respectively. Atomic force microscopy (AFM) revealed that 316 steel had the highest roughness (Ra = 45 nm) compared to 304 and 304L (R_a = 32 nm and 34 nm). A predictive adhesion model showed that 316 steel promotes phosphate adhesion (negative free energy of adhesion), while 304 and 304L steels displayed positive free energies, indicating weaker adhesion. These findings provide key parameters for understanding phosphate fouling on solid supports.

The accurate prediction of temperature in Permanent Magnet Synchronous Motors (PMSMs) has always been essential for monitoring performance and enabling predictive maintenance in the industrial sector. This study examines the efficiency of a set of artificial neural network (ANN) models, namely Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), in predicting the Permanent Magnet Temperature. A comparative evaluation study is conducted using common performance indicators, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), to assess the predictive accuracy of each model. The intent is to identify the most favorable model that balances high accuracy with low computational cost.