[Back]

N. Ghiassi, A. Mahdavi:
"Urban Energy Modelling - Employment of Cluster Analysis Methods towards GIS-Based Building Sampling for Simulations Supported Bottom-Up Urban Energy Modeling";
Poster: Vernetzung Doktoratsinitiativen - 11.02.2016, Wien, Wiener Stadtwerke (invited); 2016-02-11.

Introduction and Motivation
Deployment of new urban energy concepts, such as distributed generation and smart grids, and efficient allocation of available resources to incentivize retrofit and densification projects necessitate information regarding the energy demand, energy supply, and improvement potential of the existing building stock, as well as computational environments that allow investigation of various intervention and change scenarios.
The presenr research focuses on the developement of a simulation supported urban energy demand model. Building performance simulation methods involve various aspecs of building performance, including occupant behavior, technological aspects, and climate conditions and deliver results with high temperoal resolution. However, they are computationally expensive. To reduce the computational domain, Multivariate Cluster Analysis methods are employed for an efficient and automated building sampling based on GIS data and standards.

Introduction and Motivation
Deployment of new urban energy concepts, such as distributed generation and smart grids, and efficient allocation of available resources to incentivize retrofit and densification projects necessitate information regarding the energy demand, energy supply, and improvement potential of the existing building stock, as well as computational environments that allow investigation of various intervention and change scenarios.
The presenr research focuses on the developement of a simulation supported urban energy demand model. Building performance simulation methods involve various aspecs of building performance, including occupant behavior, technological aspects, and climate conditions and deliver results with high temperoal resolution. However, they are computationally expensive. To reduce the computational domain, Multivariate Cluster Analysis methods are employed for an efficient and automated building sampling based on GIS data and standards.