Designing an AI-driven digital twin architecture for building energy prediction
In the pursuit of global carbon neutrality, enhancing energy efficiency in buildings — particularly during their operational phases — has become a critical objective. This paper explores the potential of AI-driven Digital Twins in building operations and adopts a Design Science Research (DSR) method...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/44656 |
| Acceso en línea: | https://doi.org/10.1016/j.jobe.2025.113966 https://hdl.handle.net/10578/44656 |
| Access Level: | acceso abierto |
| Palabra clave: | Building Automation and Control Systems Digital twins architecture Energy consumption prediction Open-source framework |
| Sumario: | In the pursuit of global carbon neutrality, enhancing energy efficiency in buildings — particularly during their operational phases — has become a critical objective. This paper explores the potential of AI-driven Digital Twins in building operations and adopts a Design Science Research (DSR) methodology to guide the development, implementation, and evaluation of a data-driven solution. A six-layer conceptual architecture is proposed to support the integration of real-time data, AI models, and control logic. As a practical instantiation of this architecture, we present an application case involving three buildings, where energy consumption is predicted using machine learning. Several regression models are tested, with CatBoost Regressor achieving R2 values above 0.92. The model is deployed as a Digital Shadow using the open-source platform Node-RED. In addition to energy forecasting, two simulated application cases demonstrate the architecture’s ability to support intelligent control strategies such as load shifting and anticipatory HVAC activation. The results validate the artifact’s effectiveness and highlight the potential of combining Artificial Intelligence and Digital Twins to improve building performance and sustainability through flexible, cost-effective, and open-source tools. |
|---|