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Research on AIoT-Based Intelligent Energy Digital Twin Platform for Zero-Energy Building

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2026, 12(2), pp.7~22
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : January 2, 2026
  • Accepted : April 7, 2026
  • Published : April 30, 2026

Gijun Han 1 Dongyeon Lee 1 Minseok Kwon 1 Jimin Park 1 jonghwan Kim 1 Jeongho Choi 1 Suyeon Bae 1 Jongwon Ko 1 Sangmin Park 1

1한국성서대학교

Accredited

ABSTRACT

With the global increase in electricity demand and growing concerns over climate change, improving energy efficiency and reducing carbon emissions in buildings have become critical challenges. Effective energy management during the building operation phase requires real-time, data-driven analysis and control. This paper proposes an AIoT (Artificial Intelligence of Things)–based intelligent energy digital twin platform to address these needs. The proposed system adopts a three-layer architecture consisting of a sensor layer, an edge layer, and a data management and service layer, enabling real-time acquisition and processing of environmental and occupancy data. A YOLO-based object detection model is deployed on edge devices to estimate occupancy from camera streams, which is integrated with environmental sensor data to accurately reflect space-level energy usage characteristics. In addition, a digital twin–based energy model is developed to replicate building energy consumption patterns in a virtual environment and derive rule-based energy-saving guidelines. The proposed platform provides a scalable framework that can be extended to renewable energy and external energy systems, contributing to building-level energy optimization and future smart campus and smart city applications.

Citation status

* References for papers published after 2024 are currently being built.