A Low-Code Digital Twin Framework for IEQ-Guided Fabric-First Retrofit Decision-Making in Existing Buildings

Decarbonization of existing buildings is obstructed by the performance gap between intended and operational energy consumption. Smart energy management and monitoring of existing buildings through digital twins pose significant attributes towards decarbonization efforts. However, there is limited research that transforms digital twins’ monitored performance into actionable retrofitting strategies. Hence, this research develops a framework that bridges the digital twin concept with standards-based IEQ analytics, guiding retrofit decision-making in existing buildings. The framework offers a low-code workflow that uses Autodesk Tandem to develop a digital twin integrating indoor environmental quality (IEQ) data, including thermal comfort and air quality. IEQ is monitored since inefficient management of its parameters often results in excessive HVAC demand, contributing to the performance gap. The framework structures IEQ parameter evaluations against benchmarks guided by ASHRAE to identify deviations indicative of operational inefficiencies in energy consumption. The digital twin model positions live IEQ tracking and analysis as diagnostic measures, leading to targeted fabric-oriented retrofit prioritization. The framework was tested on a case study in a hot arid climate, where its results indicate that the integration of digital twin-based IEQ analysis with building characteristics effectively identified the need for targeted envelope improvements, including high-performance glazing, external shading elements, and sound isolation, as key factors for eliminating overheating and high noise levels. Validating the proposed retrofits’ effectiveness, energy simulations examines the whole building to find an 11.52% annual reduction in energy use intensity from 145.61 kWh/m2·year to 128.84 kWh/m2·year through shading elements and low-E films for glazing.