Detecting and locating faults in electrical cables has been a permanent concern regarding electrical power distribution systems. Over time, several techniques have been developed aiming to manage these faulty situations in an efficient way. These techniques must be fast, accurate, but, above all, efficient. This paper develops a new approach for detecting, locating, classifying, and predicting faults, particularly in different types of short-circuits in electrical cables, based on a robust artificial neural network technique. The novelty of this approach lies in the ability of the method to predict fault’s location and type. The proposed method uses the Matlab and Simulink platform and comprises four consecutive stages. The first one is devoted to the development of the Simulink model. The second one implies a large number of simulations in order to generate the necessary dataset for training and testing the artificial neural network model (ANN). The following stage uses the ANN to classify the location and the type of potential faults. Finally, the fourth stage consists of predicting the location and the type of future faults. In order to reduce the time and the resources of the simulation process, a virtual machine is used. The study reveals the efficiency of the method, and its ability to successfully predict faults in real-world electrical power systems.

We face a significant challenge in balancing the creation of comfortable built environments with the pressing need to minimize energy consumption and environmental impacts. Meeting this challenge demands a proactive approach. This research explores the interplay between indoor environmental quality (IEQ), energy use, and environmental impacts in an educational building throughout an academic year. The methodology integrates experimental campaigns for the assessment of IEQ parameters, the analysis of data on energy consumption, and the environmental impact calculations and simulations. The IEQ monitoring results for the academic year reveal a mean indoor air temperature of 26.49 °C, a CO₂ concentration of 805.83 ppm, an illuminance of 335.83 lx, and a sound level of 51.03 dB. To assess the building’s compliance with the energy efficiency regulations, the energy use intensity was calculated to be 90.19 kWh/m²/year, The environmental impact assessment revealed a global warming potential of 120,199.82 kg CO₂e/year, with natural gas consumption contributing 61.72%. The analysis of the results uncovered challenges in IEQ and opportunities for improvement. A 1 °C reduction in the indoor temperature during the heating months may result in environmental benefits, including a potential decrease of 1.17 kg CO₂e/m². This study recommends integrated, intelligent control systems and a holistic strategy to optimize the energy use while maintaining the proper IEQ in educational settings.