AI-Driven Anomaly Detection in IoT-Enabled HVAC and Water Heating Systems

Author(s)	Vignesh Alagappan
Country	United States
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.
Keywords	IoT, HVAC systems, anomaly detection, machine learning, predictive maintenance, smart buildings, water heating, energy efficiency, digital twin, cybersecurity.
Field	Engineering
Published In	Volume 16, Issue 2, July-December 2025
Published On	2025-12-13
Cite This	AI-Driven Anomaly Detection in IoT-Enabled HVAC and Water Heating Systems - Vignesh Alagappan - IJAIDR Volume 16, Issue 2, July-December 2025. DOI 10.71097/IJAIDR.v16.i2.1657
DOI	https://doi.org/10.71097/IJAIDR.v16.i2.1657
Short DOI	https://doi.org/hbkqrt

About IJAIDR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijaidr.com

Journal of Advances in Developmental Research