Proactive Fault Detection and Energy Optimization in HVAC Systems Using Predictive Modeling
Keywords:
Predictive Maintenance, HVAC Systems, Fault Detection, Energy OptimizationAbstract
This study presents a predictive maintenance framework for HVAC systems that leverages historical sensor data and graph-based analysis to enable proactive fault detection and energy optimization. A full year of operational data—including temperature, power consumption, and fault records—is preprocessed, analyzed, and used to extract relevant features such as rolling averages, anomaly indicators, and seasonal patterns. Predictive models, including Random Forest, Gradient Boosting, and LSTM networks, are trained to forecast potential faults and energy spikes. Simulations using the 2020 dataset demonstrate the approach’s effectiveness through accurate fault predictions, improved energy efficiency, and optimized maintenance schedules. The results highlight the framework’s ability to reduce downtime, minimize operational costs, and support data-driven decision-making in HVAC maintenance.
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