A Digital Twin Solution for Hybrid Ventilation Systems in Office Spaces

Abstract

The digital twin technology is emerging as a cornerstone of intelligent systems, particularly for office buildings, to optimize various operations that address human wellbeing, comfort, and energy consumption. This work develops a framework and evaluates a real-time digital twin (DT) solution for an office space conditioned by hybrid ventilation system that combines mechanical cooling and natural ventilation. The DT framework is based on model predictive control (MPC) of the hybrid system for optimizing worker productivity, thermal comfort, and energy performance. The optimal setting is achieved by dynamically switching among 9 ventilation modes of the office: natural ventilation, mechanical non-cooling ventilation, or air conditioning mode with temperature setpoints ranging between 20 °C and 26 °C with 1 °C increment. The framework was applied to a case study in the Mediterranean climate where a building energy model, simulating a typical office cell, was developed and validated. This model informed the developed machine learning-based predictive DT model representing the hybrid ventilation behaviour. By integrating this data-driven model with real-time indoor conditions combined with forecasted weather conditions and pre-defined occupancy profiles, the system dynamically adjusted ventilation modes to balance indoor comfort and energy consumption. Experimental testing was conducted in the experimental cell, comparing the performance of the proposed DT model against a conventional rule-based control strategy. Results demonstrated a significant reduction in total energy consumption by 13.63%, alongside a 22.14% improvement in occupant productivity, attributed to improved thermal conditions. Throughout working hours, the average Predicted Mean Vote (PMV) shifted closer to thermoneutrality, maintaining comfort within the ASHRAE-defined neutral zone. These findings highlight the potential of MPC-driven digital twins as a viable pathway for intelligent building management, achieving cost-effective operation while enhancing occupant well-being.