Research Article Building Thermal, Lighting, and Acoustics Modeling

Building Simulation

, Volume 7, Issue 4, pp 335-343

First online:

Nationwide buildings energy research enabled through an integrated data intensive

  • Kerstin Kleese van DamAffiliated withPacific Northwest National Laboratory Email author 
  • , Carina LansingAffiliated withPacific Northwest National Laboratory
  • , Todd ElsethagenAffiliated withPacific Northwest National Laboratory
  • , John HathawayAffiliated withPacific Northwest National Laboratory
  • , Zoe GuillenAffiliated withPacific Northwest National Laboratory
  • , James DirksAffiliated withPacific Northwest National Laboratory
  • , Daniel SkorskiAffiliated withPacific Northwest National Laboratory
  • , Eric StephanAffiliated withPacific Northwest National Laboratory
  • , Will GorrissenAffiliated withPacific Northwest National Laboratory
    • , Ian GortonAffiliated withSoftware Engineering Institute, Carnegie Mellon University
    • , Yan LiuAffiliated withFaculty of Engineering and Computer Science, Concordia University

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Modern workflow systems can enable scientists to run ensemble simulations at unprecedented scales and levels of complexity, allowing them to study system sizes previously impossible to achieve. However as a result of these new capabilities the science teams suddenly also face unprecedented data volumes that they are unable to analyze with their existing tools and methodologies in a timely fashion. In this paper we describe the ongoing development work to create an integrated data intensive scientific workflow and analysis environment that offers researchers the ability to easily create and execute complex simulation studies and provides them with different scalable methods to analyze the resulting data volumes. The capabilities of the new environment are demonstrated on a use case that focuses on building energy modeling. As part of the PNNL research initiative PRIMA (Platform for Regional Integrated Modeling and Analysis) the team performed an initial 3-year study of building energy demands for the US Eastern Interconnect domain. They are now planning to extend to predict the demand for the complete century. In the 3-year study the team simulated 2000 individual building types for 100 independent climate similar regions (600 000 individual runs) raising their data demands from a few MBs to 400 GB for the 3-year study.


building energy data analysis scientific workflow data intensive data management Hadoop RHIPE