Digital twin implementation based on a white-box building energy model: A case study on blind control for passive heating

Abstract

Digital twins (DTs) offer a powerful means of managing buildings more energy-efficiently. Anchored in Directive (EU) 2024/1275, which encourages the use of DTs to improve a building’s smart-readiness, this study investigates how a DT can optimise passive heating in an educational building through intelligent shading control. The goal is to cut heating demand by maximising solar gains during unoccupied periods, particularly at weekends, via adaptive blind operation. A calibrated white-box Building Energy Model (BEM) forms the virtual core of the DT and is examined at two calibration levels: envelope-calibrated (Model A) and convection-calibrated (Model B). Using forecast weather data, the DT generated dynamic, zone-specific blind schedules for three successive weekends. These schedules were then evaluated with real weather data in simulation and compared with fixed ‘open’ and ‘closed’ blind positions. Across all periods, the DT-derived schedules consistently lowered heating energy demand relative to the static cases. Under real weather conditions, average savings reached 3.80 % compared with the open schedule and 22.20 % compared with the closed schedule. Although forecast errors introduced some variability, the control strategy proved robust and adaptable. Model B delivered the greatest predictive accuracy, reliably capturing zone-level thermal dynamics. The results highlight the operational benefits of DT: significant energy savings, predictive control without extensive sensor deployment, and straightforward implementation costs. Taken together, these findings underline the value of DTs as practical, adaptive tools for energy-efficient building operation. © 2025 The Author(s).