Statistics put it clear that presently, cities are responsible for over two-thirds of the world’s energy consumption and account for more than 70% of global carbon dioxide emissions. To this effect, the energy consumption of a building therefore plays a crucial role in climate change. Researchers have already proposed the transformation of old existing energy systems and adoption of renewable energy sources such as wind and solar. To enable and structure this transformation process, an analysis of the actual building stock is necessary to identify and quantify, for example, the energy demand, the refurbishment potential of buildings, the usage of decentral heating supply options or the expansion potential of district heating networks. To this end, modeling of whole urban districts becomes necessary. This, however, calls for utilization of an automated process that can parameterize simulation tools.
Peter Nageler and colleagues from the Institute of Thermal Engineering at Graz University of Technology in Austria presented a new validated methodology for fully automated building modeling within urban districts based on publicly available data. In their quest, the researchers used dynamic building models with detailed heating systems to simulate heating load profiles. Their work is now published in the research journal Energy.
The research method entailed the creation of dynamic building models with detailed heating systems in the simulation environment IDA ICE. Next, the researchers employed geographical information system (GIS) software in data collection, data processing and visualization of the results thereafter. They then described the data storage procedure in a PostgreSQL database. Finally, the team validated the building simulation model with consumption data available from 69 buildings located in the city of Gleisdorf (Austria).
It was observed that the results of the annual heating and domestic hot water demand displayed a good approximation to the measurement data with a mean deviation of -0.98%. In addition, after extending the urban simulation process to the whole community with its 1945 buildings, the authors of this paper realized that the simulation model was flexibly expandable at any level of detail. More so, this meant that the simulation model was flexible enough for the addition of new buildings and for changing the old building stock data in order to refine the model easily at any level of detail.
Peter Nageler and colleagues study has successfully presented the validation of a novel methodology for fully automated building modelling within urban districts based on publicly available data. It has been seen that the proposed method can predict the energy demand of the building stock and can examine the dynamic interactions between buildings and a district heating network, which can also be modelled in IDA ICE or in another dynamic simulation tool and the required data can be exchanged via co-simulation. In conclusion, the method helps to model and quantitatively describe current building stock in an efficient and timesaving way and enables to develop future smart energy systems, in which the buildings interact with the district heating networks, with limited effort.
P. Nageler, G. Zahrer, R. Heimrath, T. Mach, F. Mauthner, I. Leusbrock, H. Schranzhofer, C. Hochenauer. Novel validated method for GIS based automated dynamic urban building energy simulations. Energy, volume 139 (2017) page 142-154
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