As Europeans spend approximately 90% of their time indoors, the quality of confined spaces is a major concern for healthy indoor environments in Europe and it has a decisive impact on people’s health, comfort and productivity. TwinBAS harnessed the potential benefits of recent technological advancements in distributed sensing, pervasive computing, context-awareness and machine learning and proposed a holistic assessment of a cvil building, resting on three main pillars: (i) indoor environmental conditions; (ii) energy performance; and (iii) the smartness of the building. Digital Twins were developed to improve Indoor Environmental Quality (IEQ) within building spaces based on both individual and collaborative user-preferences, by using real data processing with machine learning techniques and hybrid models that combined physics-based simulations. The Digital Twins enabled system automatically informed a number of control functions for optimizing building operations, with a long-term objective of creating buildings that are comfortable and healthy for the occupants yet also energy efficient.

TwinAIR was implemented across six diverse pilot sites in Europe (ES, IE, UK, SE, DE, EL) with demonstrations covering residential dwellings, public administration buildings, hospitals and schools, along with selected types of vehicles (buses, vans). It provides rich evidence to transport planners, facility managers and policymakers about factors influencing IAQ and effective interventions for mitigating its effects on health and wellbeing. The project democratises cutting edge innovation in sensors, digital twinning and visual analytics, TwinAIR enables better decision-making about future mobility policies, built environment management and incentivisation of citizens.

People

Prof. Ajith Kumar Parlikad

Dr. Stylianos Karatzas