A data-driven building thermal zoning algorithm for digital twin-enabled advanced control

Effective control of indoor environments is crucial for maintaining occupant comfort and optimizing energy use. However, current building control strategies often fail to achieve these goals, as they rely on static or rule-based approaches that normally do not account for dynamic conditions. While advanced control strategies offer a more adaptive solution, their implementation is challenging due to the need for accurate thermal models, which are resource-intensive to develop. Defining building thermal zones can help to strike a balance between model accuracy and the cost of their development and implementation. However, data-driven approaches for identifying thermal zones remain scarce. This study addresses these gaps by proposing a reusable data-driven thermal zoning algorithm that employs Principal Component Analysis (PCA) and k-means clustering to define building thermal zones. This method allows for the inclusion of numerous parameters, thus increasing the applicability and consistency of the zoning process. Additionally, we propose an algorithm for zones validation, supported by qualitative criteria from literature and standards. The approach is tested in a large educational building, using time-series data from 168 rooms with a total of 262 CO2 and temperature sensors. Results show that the proposed zoning algorithm achieves over 91 % consistency score, depending on the number of selected principal components, clusters, and input parameters available. The derived thermal zones are further validated based on the synthesised qualitative criteria. Finally, the results are visualized in a DT environment, where users can explore color-coded thermal zones alongside real-time sensor data, 3D building geometry, and semantic information.