The current standard ÖNORM EN 16798-1:2019 11 01[1] describes various classes of indoor climate, which serve as the basis for the energy planning simulation of buildings. To date, however, there are no integrated measurement systems for simple verification of the degree of fulfilment of the plans and for holistic indoor assessment including the dynamic aspects of air quality, light, acoustics and thermal comfort. ÖNORM EN 16798-1 uses two thermal comfort models: the model of ÖNORM EN 7730 (Fanger model) for air-conditioned rooms and an adaptive comfort model for non-mechanically cooled and ventilated rooms. This distinction is rather arbitrary, which is why efforts have been made in recent years in the field of research to integrate these two approaches. One possible model is the adaptive thermal balance model (ATBH) from Schweiker[2], which is very close to the comfort model of ÖNORM EN 7730, but extends it with the help of a machine-learning approach based on the ASHRAE Global Thermal Comfort Database to include parameters that reflect increased human adaptation (behavioural, psychological and physiological adaptation). The parameters were also extended to building types and building services systems in an expanded model (ATHBx). In the FLUCCO+project, it was experimentally confirmed that this comfort model provides a significantly higher prediction accuracy for the thermal sensation vote PMV than the standard calculation. The DyNaBe project will therefore continue to work with Schweiker's model and now also explore the possibilities of the extended ATHBX -model.
By implementing the ATHBx model in Python and a new series of tests in the façade box, the accuracy of the forecast for the summer case is analysed. The dynamic adaptive comfort model by Marika Vellei and Jérôme Le Dréau[3] is used as a comparative model. The project will develop measurement sets with which the extended ATHBx model and the Vellei model can be tested reliably and cost-effectively. The evaluation and prediction algorithms are to be implemented in the Python scripting language to make them easier to read, less maintenance-intensive and easier to expand. It is planned to make the script code available as open source.
For the Tiled Stove Association, the ATHBx model enables field research tests with an expected significantly higher forecasting accuracy for thermal sensation in winter, as the standard model of ÖNORM EN 7730 cannot map the psychological adaptation factor of a tiled stove and therefore delivers significantly higher PPD values (Predicted Percentage of Dissatisfied) than corresponds to reality. The new comfort model can then also be used to determine more precise forecasts of the required fuel quantity depending on the building quality, the weather and the comfort expectation using a machine learning approach.
The establishment of the new comfort models in practice would be very important, as the fluctuating energy generation of renewable energy sources can only be utilised intelligently and efficiently with this approach, without having to plan and implement "comfort losses" according to the standard models or oversized heating and cooling systems. The new comfort models also offer more freedom in the planning and implementation of low-tech solutions without compromising comfort in practice. This would help the new comfort models to spread more quickly and become state of the art. This option will become increasingly important, especially for refurbishment solutions and intelligent, cosy "cooling solutions".
[1] EN 16798-1: 2019 11 01: Energetische Bewertung von Gebäuden - Teil 1: Eingangsparameter für das Innenraumklima zur Auslegung und Bewertung der Energieeffizienz von Gebäuden bezüglich Raumluftqualität, Temperatur, Licht und Akustik - Module M1-6
[2] Schweiker M. (2022): Combining adaptive and heat balance models for thermal sensation prediction: A new approach toward a theory and data-driven adaptive thermal heat balance model. Indoor Air. 2022;32:e13018. doi:10.1111/ina.13018
[3] Vellei M., Le Dréau J.: A novel model for evaluating dynamic thermal comfort under demand response events. Building and Environment Vol. 160 (2019), 106215
