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Title: Structured building model reduction toward parallel simulation

Building energy model reduction exchanges accuracy for improved simulation speed by reducing the number of dynamical equations. Parallel computing aims to improve simulation times without loss of accuracy but is poorly utilized by contemporary simulators and is inherently limited by inter-processor communication. This paper bridges these disparate techniques to implement efficient parallel building thermal simulation. We begin with a survey of three structured reduction approaches that compares their performance to a leading unstructured method. We then use structured model reduction to find thermal clusters in the building energy model and allocate processing resources. Experimental results demonstrate faster simulation and low error without any interprocessor communication.
 [1] ;  [1]
  1. Cornell University
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Conference: 13th International Conference of the International Building Performance Simulation Association
Research Org:
Cornell University, Ithaca, NY
Sponsoring Org:
USDOE National Energy Technology Lab; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
Contributing Orgs:
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING model reduction; parallel simulation; structured model reduction; aggregation; clustering