Development of an Intelligent Predictive Maintenance and Energy Optimisation Framework for HVAC Systems Using Machine Learning and Real-Time Equipment Performance Data

This study develops a complete research framework for intelligent HVAC predictive maintenance and energy optimisation from the evidence and concepts contained in the original manuscript. The synthesis shows that four capabilities must operate together: high-quality real-time data, ML-based condition intelligence, maintenance decision support and health-aware energy optimisation. The proposed closed-loop architecture converts sensor data into maintenance and control actions and evaluates success through prediction, reliability, energy and comfort metrics. The article therefore moves beyond a descriptive discussion toward a research-testable framework suitable for implementation and empirical validation. The next stage should use real operational HVAC data so that model accuracy, downtime reduction, energy savings and cost effectiveness can be quantified without relying on assumed results.

1. Almazam, K., Humaidan, O., Shannan, N. M., Bashir, F. M., Gammoudi, T., & Dodo, Y. (2025). Innovative energy efficiency in HVAC systems with an integrated machine learning and model predictive control technique: A prospective toward sustainable buildings. 2. Jerald, F., Gopalan, A., Shanmugasundaram, V., Santhoshi, B., & Sundaram, K. M. (2024). Enhancing energy efficiency in HVAC systems through intelligent control strategies. 3. Jin, J. (2025). Enhancing manufacturing performance with AI and machine learning: Applications in predictive maintenance and production optimization. 4. Kaur, C., & Sharma, A. (2025). Enhancing predictive maintenance with AI: Applications and impact. 5. Köse, A., Sukhanov, I., Belikov, J., Tepljakov, A., & Petlenkov, E. (2024). Demand side management for commercial buildings: Multiple heating systems based optimization in real-time. 6. Kumar, C. P., Jagadeeswari, B., Naveen, G., Sai, K. P., & Babu, M. M. (2025). Predictive maintenance system using machine learning and FastAPI. 7. Moveh, S., Merchán-Cruz, E. A., Ibrahim, A. O., Elhassan, Z., Abdelhai, N. M. R., & Abdelrazig, M. D. (2025). Thermodynamic optimization of building HVAC systems through dynamic modeling and advanced machine learning. 8. Nikolaevich, E. M. (2025). Overview of the use of artificial intelligence in predictive maintenance and state management of technical systems. 9. Nwulu, E. O., Elete, T. Y., Erhueh, O. V., Akano, O. A., & Omomo, K. O. (2023). Machine learning applications in predictive maintenance: Enhancing efficiency across the oil and gas industry. 10. Reshma, R., Divya, A., & Dharneeshwaran, D. (2025). Predictive maintenance for industrial equipment using machine learning.