1Assistant Professor, Department of Electronics and Communication Engineering, Narasaraopeta Engineering College, Narasaraopet, Palnadu, Andhra Pradesh, India.
2Department of Electrical and Electronics Engineering, Faculty of Engineering, Istanbul Aydin University, Istanbul, Türkiye
3Department of Engineering Mathematics, College of Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Guntur AP, India
4Department of Computer Science, Sri Guru Gobind Singh College of Commerce, University of Delhi, New Delhi, India.
5Assistant Professor, Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
6Assistant Professor, Department of E&Tc Engineering, Mit Academy of Engineering Pune, Pune, Maharashtra, India
4Orcid Id: https://orcid.org/0000-0002-9811-3278
Background: The rapid expansion of smart buildings has created an urgent need for intelligent energy management solutions capable of addressing increasing energy demands, operational inefficiencies, and sustainability goals. Internet of Things (IoT)-based Energy Management Systems (EMS) have emerged as a central technological approach for achieving real-time monitoring, responsive control, and adaptive energy optimization. However, the complexity of heterogeneous sensor networks, occupant behavior patterns, and fluctuating environmental conditions requires advanced computational methods to extract meaningful insights and ensure optimal system performance.
Objective: This research examines the integration of machine learning algorithms into IoT-enabled EMS, evaluating their capacity to enhance predictive accuracy, improve energy utilization, and support autonomous decision-making in smart building environments. The study analyzes a broad spectrum of machine learning techniques, including supervised, unsupervised, and reinforcement learning models, to understand their respective contributions to load forecasting, anomaly detection, consumption pattern recognition, and dynamic control strategies.
Findings: Experimental simulations and real-world case evaluations demonstrate that machine learning-driven EMS can significantly reduce energy waste by accurately predicting demand peaks, optimizing HVAC operations, regulating lighting systems, and facilitating proactive maintenance. Moreover, the research highlights the critical value of hybrid models, where the combination of algorithmic strengths, such as deep learning's pattern extraction and reinforcement learning's adaptive policy formation, results in improved system responsiveness and operational resilience. A key focus of the study is the role of continuous data streams generated by IoT sensors, which enable machine learning models to learn environmental and behavioral variations over time. Findings reveal that the synergy between real-time data acquisition and intelligent analytics enhances the EMS's ability to make context-aware decisions, improving energy efficiency without compromising occupant comfort.
Challenges and Recommendations: The investigation also addresses practical challenges, including issues of data privacy, integration complexity, sensor calibration, network reliability, and scalability across diverse building architectures. Recommendations are provided for designing effective, secure, and adaptable IoT-machine learning ecosystems for future smart buildings.
Conclusion: Overall, the research underscores the transformative potential of machine learning in optimizing IoT-based energy management, offering a pathway toward sustainable, energy-efficient, and technologically resilient built environments. The insights generated aim to guide architects, building managers, engineers, and policymakers in implementing advanced EMS frameworks that support global energy conservation goals while accommodating the evolving demands of intelligent infrastructure.
Keywords: IoT-based energy management; Smart buildings; Machine learning optimization; Energy efficiency; Predictive analytics
How to cite this article: Reddy YJ, Kurt H, Tiwari A, Kaur U, Lade SG and Waghmare V, Optimization of IOT-Based Energy Management Systems Using Machine Learning Algorithms in Smart Buildings. Int J Drug Deliv Technol. 2026;16(12s): 721-729. DOI: 10.25258/ijddt.16.12s.86
Source of support: Nil.
Conflict of interest: None