Traditionally, building service engineering has had a strong focus on energy efficiency, which leads to non-satisfying comfort conditions for the user on some occasions.
Additionally, room and operating conditions are currently insufficiently recorded and recognized, as the number of sensors used and the physical values measured are limited.
Goals and innovation:
This project sets out to maximise the experienced user comfort by monitoring room conditions, considering human comfort, energy efficiency, and operational efficiency alike. The overall objective is to gain precise knowledge of states of thermal comfort and indoor air quality by approaches new to building and service technology. Room condition measurement (with control systems and in-situ comfort measurements alike) provides input data for simulations of room, services and control of different scenarios with changing boundary conditions.
The multimodule data corpus is the basis for a coupled simulation and machine learning assisted data analytics yielding new models for comfort assessment and prediction. Accompanying measurement data and simulation are dynamically coupled (hardware in the loop HIL).
Findings will be obtained by newly available leveraging technology capable of investigating factors which are traditionally not considered in the field of building service engineering. New types of sensors and measuring systems offer flexible solutions at reasonable prices, recording more room condition parameters than ever. Application scenarios will involve different working environments representing broad, complex and challenging conditions. Big Data technologies facilitate the data collection, pre-processing and analysis of measured data from sensor systems. This data can be merged with existing data sources, including building engineering systems, room management systems, scheduling systems and weather forecasts. The raw data itself, along with extracted insights, correlations, and detected patterns is used as
an input for modelling and simulating user comfort while considering the important constraints like energy efficiency or room occupation rate.
Additionally, Building Information Modelling (BIM) will be investigated to derive recommendations on how BIM data can be included into simulations separately. Virtual sensors are an essential feature, allowing more flexible and target oriented room condition monitoring than physically installed sensors, making simulation results more tangible and simplifying the illustration of the simulation results for users and operators.
A very important aspect of the project is to provide feedback for users and operators in order to achieve a better understanding of building performance and operate the building in a more human centred way.