Fault Prediction using Artificial Intelligence Data and Perovskite Material Technology

This paper focuses on a thorough analysis of a research model aimed at fault detection in streetlighting systems, using a synthetic dataset describing the performance of streetlights. An in-depth study of the application of artificial intelligence for predicting different types of faults and a thorough evaluation of the performance of the developed model is proposed. Additionally, the paper elaborates on the promising application of perovskite materials in the context of optimizing sensors and other components of street lighting systems. Special attention is given to the potential of perovskites to improve the accuracy and speed of fault detection. The aim of the study is to propose an innovative approach to fault detection and management in street lighting systems. The application of advanced technologies and materials to improve maintenance efficiency and reduce repair and maintenance costs of such systems is considered. The work aims to form practical recommendations and suggestions for the industry, in order to create more sustainable and efficient street lighting systems in the future.