Currently, concentrated solar power (CSP) plant has gained large importance due to its good environmental aspect. However, the process of the CSP power production presents important variability, as it relied on meteorological conditions, which gives new challenges to power system operators. To handle these challenges, it is significant to be able to anticipate and observe production levels. The present paper presents a comparison between two artificial intelligence models artificial neural networks (ANN) and adaptive neurofuzzy inference system (ANFIS), to estimate the daily electric energy generation of a solar power plant in Ain Beni-Mathar (34°00'35”N, 2°01 `29”W) in Eastern Morocco. Six input variables (daily direct normal irradiation, day of the month, daily mean ambient temperature, mean wind velocity, relative humidity and previous daily electric production) served as the inputs of the ANN and ANFIS models. The results showed that both the ANN and ANFIS models performed similarly in terms of prediction accuracy.

A numerical simulation is investigated to enhance the freshwater productivity of the modified solar still desalination system. A phase change material (PCM) is incorporated under the absorber plat as a thermal energy storage medium. Nanoparticles (Al ₂ O ₃ ) have been added to the saline water and the whole system is covered by a transparent glass. Energy conservation equations were solved numerically by using Comsol software. Calculations have been done for three nanoparticle concentration values (0.02%, 0.1% and 0.2%) and several PCM quantities (10kg, 15kg, 20kg and 25kg). The model considered is validated and the influence of these two last parameters on the freshwater productivity is highlighted. the results indicated that the daily freshwater reached a maximum value of 7.74kg/m ² with mpcm=20kg, compared to 6.67kglm ² for the modified solar still with nanofluid and without PCM, and to 5.23kglm ² for the conventional solar still.

The aim of the present study is the use of Artificial Neural Networks (ANN) modeling to estimate the hourly based electric energy generation of a Parabolic Trough Solar Thermal Power Plant (PTSTPP), located in Eastern Morocco. Data covering 4 years are used in order to train and validate a three Multi-Layers Perceptron (MLP) model. In order to choose the best architecture, several statistical criteria are used such as: Coefficient of Correlation (R), Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Relative Mean Bias Error (RMBE) and Mean Absolute Error (MAE). The back propagation learning algorithm is used to train different ANN architectures. Predicted results indicates that the total electric energy accumulated for the validation year was about 42.6 GWh/year, representing an underestimation less than 5% from the recorded energy. The results indicate that the ANN model can successfully estimate the energy production of a solar power plant with parabolic trough collectors.

Urban heat vulnerability is an increasing public health concern, particularly in rapidly urbanizing regions of southern France. This study aims to quantify and map the Heat Vulnerability Index (HVI) for Toulouse and to analyze its temporal trends to identify high-risk zones and influencing factors. The assessment integrates recent years’ remote sensing data of pollutant emissions, land use/land cover and land surface temperature, statistical data of climate-related mortalities, and socioeconomic and demographic factors. Following a detailed analysis of recent real-time air quality and weather data from multiple monitoring stations across the city of Toulouse, it was observed that Urban Pollution Island (UPI) and Urban Heat Island (UHI) are closely interlinked phenomena. Their combined effects can significantly elevate the annual mortality risk rate by an average of 2%, as calculated using AirQ+ particularly, in densely populated urban areas. Remote sensing data was processed using Google Earth Engine and all factors were grouped into three key categories: heat exposure, heat sensitivity, and adaptive capacity to derive HVI. Temporal HVI maps were generated and analyzed to identify recent trends, revealing a persistent increase in vulnerability across the city. Comparative results show that 2022 was the most critical summer period, especially evident in areas with limited vegetation and extensive use of heat-absorptive materials in buildings and pavements. The year 2024 indicates resiliency and adaptation although some areas remain highly vulnerable. These findings highlight the urgent need for targeted mitigation strategies to improve public health, enhance urban resilience, and promote overall human well-being. This research provides valuable insights for urban planners and municipal authorities in designing greener, more heat-resilient environments.