AI-Driven Digital Twin Architecture for Building Energy Prediction
In the global effort toward achieving carbon neutrality, the building sector stands as one of the largest contributors to energy consumption and emissions. Enhancing the energy efficiency of buildings, particularly during their operational phase, has therefore become a central focus of modern architecture and sustainability research. The integration of Artificial Intelligence (AI) and Digital Twin technologies presents a promising path forward, enabling data-driven insights, real-time control, and predictive energy management. This research investigates the role of AI-driven Digital Twins in optimizing building performance, aligning technological innovation with sustainable design principles.
Research Motivation and Objectives
Buildings account for a significant portion of global energy demand, and inefficiencies in operation often stem from the lack of real-time monitoring and predictive control systems. Traditional energy management approaches fall short in dynamically adapting to varying conditions and user behaviors. The motivation of this study is to bridge this gap by developing an AI-enhanced Digital Twin framework that integrates continuous data collection, intelligent forecasting, and autonomous control. The primary objective is to design, implement, and evaluate an open-source, flexible architecture capable of improving energy efficiency and reducing operational costs.
Methodological Framework: Design Science Research (DSR)
This research adopts the Design Science Research (DSR) methodology, which emphasizes the creation and evaluation of innovative artifacts to solve real-world problems. Within this framework, the study progresses through iterative stages—problem identification, artifact design, development, and evaluation. The DSR approach ensures both theoretical rigor and practical relevance, guiding the systematic construction of a six-layer conceptual architecture for AI-driven Digital Twin implementation in building energy systems.
Proposed Six-Layer Architecture
The proposed architecture consists of six integrated layers designed to support data acquisition, model development, intelligent control, and system evaluation. These layers include data collection, data processing, AI modeling, digital shadow deployment, control logic, and visualization. Together, they create a seamless connection between the physical and virtual building environments. By incorporating real-time sensors, machine learning algorithms, and open-source tools such as Node-RED, the architecture enables adaptive energy optimization and predictive decision-making across multiple building systems.
Experimental Implementation and Results
To validate the proposed framework, a practical application involving three buildings was conducted. Energy consumption data were collected and analyzed using various machine learning regression models, including Linear Regression, Random Forest, and CatBoost Regressor. Among these, CatBoost achieved superior performance, with R² values exceeding 0.92, demonstrating high predictive accuracy. The trained model was deployed as a Digital Shadow within the Node-RED platform, enabling real-time forecasting and intelligent control simulations. These experiments confirm the model’s reliability and scalability for diverse building types and operational conditions.
Discussion and Future Implications
The findings highlight the potential of combining AI and Digital Twin technologies to transform building energy management. Beyond prediction, the architecture supports advanced control strategies such as load shifting and anticipatory HVAC activation—contributing to flexible, energy-efficient building operations. Future research could explore the integration of renewable energy sources, occupant behavior modeling, and reinforcement learning for self-adaptive control. The open-source, modular design of this system also encourages widespread adoption, advancing the global transition toward smart, sustainable, and carbon-neutral architecture.
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