TY - GEN
T1 - Toward a Unified Architecture for Smart Home Energy Monitoring
T2 - 23rd IEEE Consumer Communications and Networking Conference, CCNC 2026
AU - Andruccioli, Manuel
AU - Olaiya, Kelvin
AU - Testa, Alex
AU - Bennici, Salvatore
AU - Vasiliu, Rares
AU - Congwen, Cui
AU - Jingzhe, Lin
AU - Keon, Lou Kuok
AU - Rui, Bao
AU - Taoyuan, Wang
AU - Xinyuan, Cheng
AU - Yi, Zhang
AU - Salomoni, Paola
AU - Ghini, Vittorio
AU - Lam, Chan Tong
AU - Tang, Su Kit
AU - Delnevo, Giovanni
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - The increasing deployment of smart devices in residential environments opens new opportunities for intelligent energy management. However, existing platforms often fall short in providing intuitive interfaces, zone-level control, and advanced predictive analytics accessible to non-expert users. This paper presents the design of a modular smart home energy management system that integrates real-time monitoring, consumption forecasting, and intelligent assistance via Large Language Models (LLMs). The system features an interactive floor plan interface, multi-user support, threshold-based alerting, and detailed historical analytics. Additionally, it introduces LLM-powered agents that guide users in configuring smart devices and adopting more efficient consumption behaviors. This architecture emphasizes accessibility, adaptability, and extensibility, aiming to empower users with actionable insights and seamless device management. The proposed solution addresses current gaps in existing platforms and lays the groundwork for intelligent, personalized, and proactive home energy systems.
AB - The increasing deployment of smart devices in residential environments opens new opportunities for intelligent energy management. However, existing platforms often fall short in providing intuitive interfaces, zone-level control, and advanced predictive analytics accessible to non-expert users. This paper presents the design of a modular smart home energy management system that integrates real-time monitoring, consumption forecasting, and intelligent assistance via Large Language Models (LLMs). The system features an interactive floor plan interface, multi-user support, threshold-based alerting, and detailed historical analytics. Additionally, it introduces LLM-powered agents that guide users in configuring smart devices and adopting more efficient consumption behaviors. This architecture emphasizes accessibility, adaptability, and extensibility, aiming to empower users with actionable insights and seamless device management. The proposed solution addresses current gaps in existing platforms and lays the groundwork for intelligent, personalized, and proactive home energy systems.
KW - Digital Sustainability
KW - Human-Centered IoT
KW - Intelligent Energy Management
KW - Machine Learning for Smart Homes
KW - Smart Home Automation
KW - Web of Things Integration
UR - https://www.scopus.com/pages/publications/105034195721
U2 - 10.1109/CCNC65079.2026.11366485
DO - 10.1109/CCNC65079.2026.11366485
M3 - Conference contribution
AN - SCOPUS:105034195721
T3 - Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
BT - 2026 IEEE 23rd Consumer Communications and Networking Conference, CCNC 2026
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 January 2026 through 12 January 2026
ER -