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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 107
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Paper 35

ISES Virtual Energy Laboratory: A Cloud-Based System for Energy-Efficient Building Design and Simulation

R. Klinc1, R. Hoch2, K. Baumgärtel2, P. Katranuschkov2, T. Laine3 and M. Dolenc1

1Faculty of Civil and Geodetic Engineering, University of Ljubljana, Slovenia
2Technical University of Dresden, Dresden, Germany
3Granlund Oy, Finland

Full Bibliographic Reference for this paper
, "ISES Virtual Energy Laboratory: A Cloud-Based System for Energy-Efficient Building Design and Simulation", in , (Editors), "Proceedings of the Fourth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 35, 2015. doi:10.4203/ccp.107.35
Keywords: energy-efficient design, civil engineering, life cycle simulation, virtual laboratory, cloud computing, building information model, high-performance computing, high-throughput computing, ISES.

An important domain of energy and emission reduction is the phase of the planning of new buildings and facilities or such to be refurbished. Another important domain is the design of new products. There are several identified gaps in the current information-communication technology support of energy-efficient design and operation (e.g. from interoperability between energy analysis and building design tools to the use of stohastic analysis for energy life-cycle calculations) that will be addressed by 7th FP EU project Intelligent Services for Energy-Efficient Design and Life Cycle Simulation (ISES).

The paper describes the implementation details of the ISES Virtual Energy Laboratory platform including developed services and tools, provided ISES Cloud application programming interface that enables higher-level use of the underlying cloudbased high-performance/high-throughput computing resources. It also provides detailed description of the end-user scenarios that were used to evaluate the developed systems as well as performance benchmarks of the system that show the benefits of using different computing environments for enabling stochastic energy analyses.

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