Novel validated method for GIS based automated dynamic urban building energy simulations

Significance 

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.

Novel validated method for GIS based automated dynamic urban building energy simulations. Advances in Engineering

Novel validated method for GIS based automated dynamic urban building energy simulations 2Novel validated method for GIS based automated dynamic urban building energy simulations. Advances in Engineering

About the author

Peter Nageler is a PhD student at the Institute of Thermal Engineering, Graz University of Technology, Austria. He got there his master degree in Mechanical Engineering in 2014. He develops in his research project “EnergySimCity” simulation methods for urban energy systems, which are able to capture the dynamic interactions between buildings, energy supplier and district heating networks at urban scale.

About the author

Franz Mauthner is researcher at AEE INTEC in Gleisdorf, Austria. Research areas comprise solar thermal applications in both industries and in connection with district heating systems as well as holistic energy system analysis and spatial energy planning. His expertise further includes project development and management of national and international R&D activities.

About the author

Gregor Zahrer is a geoinformation specialist with a strong focus on software development. His research topics center on (spatial) data modeling, webGIS development and visualization. Currently, he is working for a public utility company at the interface between GIS and network information systems.

About the author

Thomas Mach is Project Senior Scientist at the Institute of Thermal Engineering at Graz University of Technology. He holds a master degree in Architecture and a PhD in Mechanical Engineering and is developing, working on and coordinating research projects (national, European and International Energy Agency). His work deals with energy concepts for buildings, multifunctional façade technology, building renovation, thermal simulation, and the design and simulation of urban energy concepts.

About the author

Richard Heimrath is Project Senior Scientist at the Institute of Thermal Engineering at Graz University of Technology. He completed his studies there in Industrial- / Mechanical Engineering in 1999 and submitted his dissertation in 2004. He has been active in many regional, national and international projects with a focus on the implementation of solar thermal energy and the thermal simulation of buildings and plants, supervises scientific work and holds numerous lectures.

About the author

Hermann Schranzhofer finished his studies of Technical Physics at Graz University of Technology in 1998. After two years’ employment as Software Engineer at KNAPP Logistics Automation GmbH he started his PhD study in material science at the Montanuniversity Leoben finishing it in 2005. Since then he works at the Institute of Thermal Engineering at Graz University of Technology as a Project Senior Scientist in many different national and international research projects.

The main focus is on development of simulation models (including models of PCM storages) and system and building simulation (using TRNSYS) with additionally using co-simulation frameworks (BCVTB).

About the author

Ingo Leusbrock (PhD) is the head of the group “On-Grid Energy Supply and System Analysis” at AEE INTEC in Gleisdorf, Austria, and is an expert in the field of modelling and simulating urban and industrial resource and energy cycles. After a PhD in Chemical Engineering at Wetsus (NL) and the University of Groningen (NL) on desalination with supercritical fluids, he has worked for several years at the University of Wageningen (NL) as lecturer and researcher. In Wageningen, he focussed on the development of system-wide approaches for resource (water, nutrients, energy, etc.) cycles and the evaluation thereof via simulation, field tests an demonstrators.

Ingo has broad experience in projects focusing on planning, developing and evaluation energy systems, in which resource cycles like heat, cold and electricity are jointly addressed and combined with renewable energy supply, storage technologies, demand/response options and spatial planning tools.

About the author

Christoph Hochenauer is Full Professor and Head of the Institute of Thermal Engineering at Graz University of Technology (since 2012), Deputy Head of the Field of Expertise “Sustainable Systems” in the Graz University of Technology (since 2013 until nowadays). He is leading numerous research projects in the field of Thermal Engineering and Fluid Mechanics and is Author (co-author) of more than 100 publications in journals and conference proceedings, as well as reviewer of several scientific journals.

Reference

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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