Augmented Green Twin for Building Energy Optimization

Abstract

Optimizing energy use in buildings has become critical as global sustainability and carbon reduction goals accelerate. This study explores the potential of an innovative framework, Augmented Green Twin (AGT), which combines digital twin technology with augmented intelligence to enhance the operational energy efficiency of buildings. Specifically, it investigates AGT as a transformative approach to optimizing building energy consumption, focusing on technical performance and user engagement. Traditional building management systems face significant limitations, including static energy modelling, poor real-time adaptability, disconnected system integration, and oversight of human behavioural factors. These challenges often result in inefficient energy use and suboptimal occupant comfort. This study develops a conceptual AGT framework to guide future energy optimization strategies based on insights from a comprehensive literature review and stakeholder interviews. The framework is designed to leverage real-time data and augmented intelligence to enable adaptive, dynamic monitoring and energy usage adjustment in response to traditional systems' identified limitations. By incorporating a variety of data inputs, such as sensor data, weather data, occupancy patterns, and user feedback, AGT can provide a more responsive and personalized energy management solution. AGT integrates predictive analytics, enabling the system to anticipate future energy demands and proactively address potential issues. It emphasizes a human-in-the-loop approach, ensuring that occupant preferences, behaviours, and comfort are actively incorporated into energy optimization strategies. It also identifies potential challenges to the practical implementation and scalability of the proposed AGT framework. It is identified that despite its potential, successful deployment of the framework will require addressing challenges regarding the integration of legacy systems, data security, and user acceptance. This study contributes to the body of knowledge by offering valuable insights into the potential and challenges of AGT for building energy optimization.