Hocheffiziente Tiny-House Muster-Siedlung

The building sector accounts for almost 1/3 of the global final energy consumption, which is mainly made up of heating and cooling, as well as domestic hot water heating, lighting and household appliances. Due to global warming, the energy demand in buildings for heating will decrease, while that for cooling will increase in future (V. Ciancio et al., Sustainable Cities and Society 60, 102213 (2020)). Energy-saving ventilative cooling strategies like e.g. night cooling or hybrid ventilation in mixed-mode buildings therefore will be favourable compared to conventional heating, ventilation and air conditioning (HVAC) systems (L.L. Gomis et al., Energy and Buildings 231, 110597 (2021)).

Our research focuses on evaluating the effectiveness of automated, window-integrated ventilation systems compared to conventional ventilation and air conditioning systems for thermal control during summertime. We use two floors of an office building as a real laboratory for this purpose. Extensive data is collected via KNX sensors installed in the building. The study evaluates scenarios with manual and controlled natural window ventilation, decentralised window-integrated mechanical systems with heat recovery and combinations of all of them. The accuracy of the KNX sensors is checked with the help of laboratory measuring devices.

By connecting to a robust database, we capture detailed environmental metrics including temperature, humidity and CO2 levels, and other parameters such as user presence. Statistical and AI-supported analyses are used to determine the comfort level, user acceptance, energy requirements, CO2 emissions and economic costs for different scenarios. The results are compared with established building performance simulation tools such as EnergyPlus or IDA ICE.

Through detailed analyses and simulations, including user behaviour and the influence of user-specific conditions, our comprehensive approach will provide a better understanding on the optimal dimensioning, configuration and operation of natural, mechanical and hybrid window-integrated ventilation systems, improving operational efficiency in terms of cycles and running times. The study emphasises the need for sensor-based parameter recording to improve the accuracy of demand forecasts and thus support the efficient and sustainable use of energy resources and provides actionable recommendations for optimizing summer thermal control through automated, window-integrated solutions.