Analysis of Predictive Maintenance Based on Machine Learning Approches for HVAC Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I2P107Keywords:
HVAC Systems, Predictive Maintenance, Machine Learning, Iot Technologies, Energy EfficiencyAbstract
Residential and commercial buildings rely heavily on HVAC systems, which are notoriously power hogs, thus it's crucial that these systems be reliable and efficient. This paper explores the concept of implementing predictive maintenance and occupancy aware control in HVAC systems to improve the energy efficiency, operational stability, and occupant comfort. It discusses how maintenance practices have changed since the reactive and preventive methods to be data-driven, predictive maintenance facilitated by IoT technologies, automatic commissioning, and machine learning. Occupancy recognition methods and how these can be used to achieve intelligent HVAC control are also examined in the study with optimal start/stop timing, ramp down approaches and adaptive pressure control. More so, the supervised and unsupervised machine learning algorithms to fault detection and diagnosis are discussed, and their ability to manage complex and nonlinear HVAC behaviors. As the results have shown, the predictive maintenance should be coupled with the optimistic use of occupancy to lower the energy consumption, minimize downtime, and prolong the life of equipment, providing a complex of measures to control the HVAC system of a building as a set of sustainable and intelligent.
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