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Variable refrigerant flow (VRF) systems are constantly prone to failures during their lifespan, causing breakdowns, high energy bills, and indoor discomfort. In addition to correctly identifying these defects, fault detection, and diagnostic studies should be able to anticipate and predict the anomalies before they occur for efficient maintenance. Therefore, this study introduces an efficient self-learning predictive maintenance system, CACMMS (Cloud Air Conditioning Monitoring & Management System), designed to anticipate refrigerant leaks in VRF systems. Unlike previous efforts, this system leverages advanced fault detection and diagnosis strategies in a real existing building to enhance prediction accuracy. The study employed three noise filtering models (Kalman filter, moving average, S-G smoothing) in the preprocessing phase. Ten features were selected for assessment, and four machine learning models (decision tree, random forest, K-nearest neighbor, support vector machine) were compared. The accuracy, precision, sensitivity, computation time as well and confusion matrix were used as performance indicators and metrics to evaluate and choose the best performant model. Results indicated that decision tree and random forest models achieved over 95 % accuracy with execution times between 0.70 s and 3.32 s, outperforming K-nearest neighbor and support vector machine models. These findings highlight the system’s potential to reduce downtime and energy costs through effective predictive maintenance.
Effective building energy efficiency requires understanding fenestration’s role in regulating indoor temperatures. Therefore, this study investigates the impact of integrating static glazing with dynamic coatings on building energy efficiency and indoor comfort in a lightweight structure situated in a semi-arid climate. Employing a comprehensive numerical model developed using EnergyPlus and Radiance tools, various static commercial glass window configurations are evaluated to assess their effects on energy consumption, thermal and visual comfort, and economic and environmental considerations. This analysis includes assessments of thermal comfort using PMV and PPD indicators and evaluations of visual comfort based on daylighting and glare metrics. The findings highlight the advantages of incorporating thermochromic and electrochromic films, demonstrating significant improvements in building energy efficiency and interior thermal and visual comfort. Notably, double glazing emerges as the most economically efficient and environmentally viable option, resulting in a reduction of emissions by 1522.38 kgCO2/year, with a payback period of 12.86 years. Furthermore, combining thermochromic and electrochromic coatings with optimal static glazing leads to a remarkable 26 % reduction in energy consumption. These results underscore the potential of dynamic coatings to enhance building energy performance while ensuring occupant comfort. This research approach provides valuable insights into sustainable building design, emphasizing the integrated impact of glazing solutions on energy use, comfort, and environmental sustainability.