Twining Buildings: A Methodological Framework for Design and Implementation using Home Assistant Technology

This paper presents a streamlined approach to developing a Digital Twin (DT) for buildings, emphasizing practical implementation and real-world applications. The primary challenge addressed is the complexity of twinning physical systems, which requires a multifaceted skill set encompassing cloud infrastructure, edge computing, the Internet of Things (IoT), distributed data management, and machine learning. Our work simplifies this process, offering a clear, step-by-step guide to creating a DT based on building 3D models. The core contribution of this research is the integration of various technologies into a cohesive DT system, monitored through the Home Assistant (HA) platform. This includes setting up custom sensors, selecting a single-board computer, configuring the HA platform, and implementing 3D building rendering. A key outcome is the use of a household ambient-energy timeseries dataset for predicting energy consumption, providing a practical application of our DT model and showcasing its utility in real-world scenarios. Our approach not only validates the DT model but also highlights its potential in enhancing home monitoring and energy management. By integrating HA with DT and 3D rendering, we offer a framework that provides comprehensive control, predictive capabilities, and smart adjustments, representing a significant advancement over existing systems. The paper concludes by identifying future research opportunities, particularly in developing machine learning models for fault detection and predictive maintenance in DT systems.