RANA MAHMOUD INTRODUCES BUILDING STOCK DATA ANALYSIS
Posted in: Blog-posts
I am Rana Mahmoud, I joined the hybridGEOTABS project in December 2016 as a PhD researcher at Ghent University. My research focuses on studying the building stock in different European countries, as an approach for analysing the energy demand for the different buildings typologies to assess the feasibility of achieving an optimal sizing for the hybridGEOTABS system. One of the aims of the hybridGEOTABS project is to design a low-cost and efficient energy system, through facilitating the predesign phase for engineers and architects to reduce time and costs for simulating case by case project. An easy-to-use procedure will be proposed based on different typologies classifications with different energy requirements that would help designers on choosing the right sizing of the system according to these classifications. The research question is how to simulate in an automated process vast amount of building stock data to obtain load duration curves that will guide the optimal sizing of the GEOTABS system components for the different building typologies.
The approach starts from building stock general data (refer to Fig.1) that are being gathered for four building typologies envisaged (offices, schools, care houses and multi-family buildings). The buildings are further classified into building sub-typologies with similar characteristics that includes location, climate and geometrical aspects as surface area ranges, attached or semi-attached buildings etc. The building stock data are generally described without details on the building form and with few information about the building envelope a difficulty for modelling the building stock. From the available general data such as building area and volume, a mathematical transformation process into measurable data as height, width and length is used. This method allows to model and parametrize the building stock data in building information model BIM. The output of this process are geometrical and zonal data for each building case. A tool was then developed to translate all the output data from the BIM model into a readable format by a building energy simulation environment Modelica in an automated way for simulation of the different building sub-typologies. This process reaching from geometrical input data towards a complete BES-model is based on the work of a Ghent University colleague Delghust[1]. It is now applied to the additional building typologies studied in the hybridGEOTABS project and towards a different BES-modelling environment based on Modelica language. Using this process, a range of geometrical variations as well as building physical properties can be modelled to experiment different design scenarios and strategies for efficient sizing of the hybrid GEOTABS system components for a variety of building typologies throughout the European climates. It’s really a pleasure to work on such an influential project.
[1] M. Delghust, Improving the predictive power of simplified residential space heating demand models, University of Ghent, 2015, http://hdl.handle.net/1854/LU-6988905