Computer scientist Anjana Mohandas Sheeladevi demonstrated that intelligent, occupant-centric energy management systems can enhance energy efficiency and user comfort in smart buildings.
The research of Mohandas Sheeladevi focused on developing an Internet of Things (IoT)-enabled, occupant-centric energy management framework that balances comfort, privacy, and sustainability in smart buildings. The scientific problem addressed was how to achieve personalized energy efficiency while ensuring occupant well-being, data privacy, and environmental responsibility. Four key research challenges guided the study:
- Designing comfort-based automated control systems through algorithms for occupant localization and spatio-temporal appliance management;
- Ensuring privacy in personalized automation by developing privacy-preserving methods for user preference data;
- Creating integrated systems for multi-occupancy buildings that optimize energy and translate consumption into personalized carbon footprints;
- Designing a sustainability-aware energy management system that accounts for multiple stakeholder needs and assesses technical, social, and environmental feasibility.
Motivated by the global transition toward net-zero buildings, this research proposed the EnSAF framework to enable scalable, context-aware, and sustainable energy solutions for smart communities.
Future-ready and sustainable
This research has demonstrated that intelligent, occupant-centric energy management systems can significantly enhance both energy efficiency and user comfort in smart buildings. The developed Multimodel Energy Management System (MEnMS) integrates IoT technologies, data analytics, and predictive control to optimize energy use dynamically while maintaining sustainability. Through real-time sensing and adaptive algorithms, the system autonomously manages building functions—such as lighting and appliance operation—according to occupants’ preferences and behavior patterns. The framework effectively balances four key dimensions: energy efficiency, cost-effectiveness, occupant well-being, and carbon footprint reduction. Implementing edge and cloud-based decision architectures ensures scalability, resilience, and adaptability to evolving energy demands. Experimental evaluation and simulations confirm substantial improvements in energy performance, operational cost reduction, and user-centric adaptability. Overall, the research establishes a pathway toward future-ready, sustainable building environments that intelligently align technological innovation with environmental and social objectives.
Reducing energy consumption
This research addresses one of today’s most pressing global challenges: reducing energy consumption and carbon emissions in buildings, which account for nearly 40% of total energy use worldwide. The results are highly relevant to architects, urban planners, policymakers, and technology developers working toward sustainable, net-zero cities. The proposed energy management framework can be directly applied in smart homes, offices, and campuses to automatically manage energy use while keeping occupants comfortable. For example, an office building using this system could dim unused lights and optimize air conditioning based on real-time occupancy—cutting energy bills and emissions without affecting productivity. Such applications could be commercially viable within the next 3–5 years as IoT-based building systems become mainstream. More broadly, the research contributes to global climate goals, smart city development, and digital sustainability initiatives by showing how intelligent, human-centered technologies can make everyday living both efficient and environmentally responsible.
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