Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards the over-represented class where windows remained closed. In addition, they require tuning for each occupant which can not be efficiently scaled to the increased number of occupants. This paper presents a window opening model for commercial buildings using deep learning methods. The model is trained using data from occupants from an office building in Germany.In total, the model is evaluated using almost 20 mio. data points from 3 independent buildings, located in Aachen, Frankfurt and Philadelphia. Eventually, the results of 3100 core hours of model development are summarized, which makes this study the largest of its kind in window states modeling. Additionally, the practical potential of the proposed model was tested by incorporating it in the Modelica-based thermal building simulation. The resulting evaluation accuracy and F1 scores on the office buildings ranged between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 % points in case of sparse input data, while the F1 score remained high.

No abstract

To accomplish the current climate goals of the federal republic of Germany, energy efficiency within the building and automotive sector must improve considerably. One possible way to reduce the high amount of energy required for heating, ventilation, and air-conditioning (HVAC) is the introduction of personal climatization systems in combination with the extension of the standardized room air temperature range. Personal systems allow improvements of climatic conditions (heating, cooling, and air quality) within sub-areas of the room instead of conditioning an entire room air volume. In this regard, personal systems are perfectly suitable for locations with local air-conditioning focal points, such as open-plan offices and vehicle cabins, where they substantially improve the energy efficiency of the entire system. This work aims to summarize previously conducted research in the area of personal climatization systems. The investigated local thermal actuators comprise fans for the generation of air movement, ventilators for the improvement of the air quality within the respiratory area of persons, water-conditioned panels for the climatization of persons via longwave radiation and conduction, radiant heaters, and combinations of the systems. Personal systems are superior to mixing ventilation regarding the improvement of the perceived air quality and thermal comfort. Furthermore, the introduced overview shows that personal climatization systems are generally more energy-efficient than conventional air-conditioning and facilitates the extension of the indoor air temperature corridor of the HVAC. Table fans and climatized seats are highly effective in connection with the improvement of personal thermal comfort. The performance of the overwhelming majority of applied personal environmental control systems is user-controlled or depends on a predefined load profile, which is generally defined person independent. Single studies reveal that effectively controlled automated systems have a similar thermal impact on a user’s thermal comfort as user-controlled ones. The implementation of an automated control system is feasible by using novel approaches such as the so-called human-centered closed loop control-platform (HCCLC-platform). The latter contains a central data server which allows asynchronous, bi-directional communication between multi-modal sensor data, user feedback systems, thermal actuators and numerical calculation models used to assess the individual thermal comfort of a person. This enables a continuous and holistic reflection of the thermal situation inside a room and the estimation of the corresponding impact on an individual’s thermal comfort. Considering the measured and simulated thermal state of a single person, the described system is capable of determining body-part-specific energy requirements that are needed to keep the overall thermal comfort level of an individual person on a high level.

No abstract

No abstract