The prevalence of extensive glazed areas in contemporary buildings contributes significantly to solar radiation infiltration, elevating energy demands and causing discomfort for occupants. Window shading devices play a pivotal role in addressing this challenge. This paper presents the development and optimization of an artificial neural network (ANN) predictive model, designed to enable real-time control of slat angles by predicting total energy loads, specifically during summer (for cooling and lighting purposes). The refined model demonstrates high precision, achieving a normalized root mean square error (nRMSE) of approximately 1.72% and a correlation coefficient (R) of around 0.999, despite utilizing limited meteorological data. Key inputs for the model include solar radiation, solar altitude, and external temperature, with a particular focus on slat reflectivity. The study assesses the efficiency of three slat types based on their reflectivity: high (80%), medium (50%), and low (20%). Additionally, the research explores the impact of window-to-wall ratio (WWR) values on the control system's efficacy, revealing a positive correlation between higher WWR values and improved energy savings through ANN slat angle control. Furthermore, the study extends the applicability of the ANN model to the six thermal zones in Morocco, affirming its generalization across diverse environmental conditions.

The Cool Roof concept, known for its efficiency in summer due to high temperatures during this period, employs a light coating that covers the roof to prevent the absorption of heat and maintain lower indoor temperatures. This study integrates a chemical component with biomaterials to enhance performance and reduce CO₂ emissions. The composition investigated in this research is recognized for its durability and ability to lower outside temperatures, thereby mitigating the urban heat island effect. This experimental study evaluates the sustainability of CoolRoofs in a cold room located in Signes, France. Temperature measurements are conducted from 25 September 2023 to 27 July 2024, both with and without the coating, to assess energy performance and CO₂ emissions. The selection of the building type ensures optimal performance in both summer and winter. Results show that the maximum outside and inside surface temperatures for a Cool Roof are 48.7 °C and 25.6 °C, respectively, compared to 72.9 °C and 32.2 °C for an uncoated roof. Additionally, implementing a CoolRoof reduces thermal load through the cold room by 56%, while CO₂ emissions can be reduced by up to 27.31 kg CO₂/m² over a 20-year period. This study presents a solution for enhancing energy and environmental performance year-round using a resilient composite.

This study focuses on optimizing the energy efficiency of polyethylene tunnel greenhouses in Mediterranean climates using Design Builder and EnergyPlus software. The research examines the impact of different greenhouse orientations and opening configurations on energy consumption for heating and cooling. The east-west (90°) orientation was found to be the most effective in reducing cooling needs during summer while slightly increasing winter heating requirements. Various opening configurations were tested, including 0%, 20%, 40%, 60%, 80%, and 100%, with openings positioned at the bottom on one side and the top on the opposite side. The 20% opening configuration was identified as optimal for annual energy management. However, a monthly adaptive planning strategy was developed to further enhance energy efficiency. This planning involves keeping the greenhouse closed during winter, using 100% openings in summer, and 20% openings during transitional months. The proposed monthly planning resulted in a significant reduction in energy consumption, decreasing daytime usage by 27.3%, night time usage by 29%, and overall annual energy consumption by 27.6%. This research demonstrates that strategic orientation and adaptive opening management can substantially improve the energy efficiency of greenhouses in Mediterranean climates.