Year: 2025
Type: article
Abstract: As buildings evolve into intelligent, connected ecosystems, Heating, Ventilation, and Air Conditioning (HVAC) systems — along with water heating infrastructure — are becoming increasingly IoT-enabled. These systems generate continuous streams of sensor data that can be harnessed to ensure energy efficiency, occupant comfort, and operational reliability. However, identifying performance degradation or faults within these complex, multi-component systems remains a challenge. This paper explores an AI-driven approach for real-time anomaly detection in IoT-enabled HVAC and water heating systems. By leveraging machine learning (ML) and advanced data analytics, the proposed framework identifies abnormal patterns, predicts potential failures, and facilitates proactive maintenance — enabling energy savings, system longevity, and sustainability.
Author: Vignesh Alagappan -
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7125271824