Real-time Air Conditioning Unit Monitoring System with Analytics for Predictive Maintenance

With the increasing reliance on air conditioning systems for indoor comfort and climate control, innovative approaches are imperative to enhance efficiency, reduce costs, and extend equipment lifespan. This study presents a comprehensive investigation into a real-time air conditioning unit monitoring system integrated with advanced analytics for predictive maintenance. Using wireless sensor networks and different learning algorithms, the advanced system is capable of collecting real-time data, including temperature, vibration, voltage and current flow from air conditioning (AC) units. It addresses problems in monitoring and performing maintenance late or excessively early in AC units. In the study, a comprehensive look at different predictive analytical algorithms is carefully identified and evaluated. The result is a system with a graphical user interface for displaying current value parameters and predictive insights. By combining these technologies, maintenance is kept to a minimum, the life of the equipment is lengthened, and continuity is ensured. This feature will significantly lessen the delay and hassle in manual monitoring of AC units in FAITH Colleges and other schools and establishments where the system may be deemed applicable and necessary. The integration of real-time monitoring with predictive analytics sets a new benchmark for AC unit maintenance, pushing the frontier of smart home and industrial applications.