TY - GEN
T1 - Estimating building simulation parameters via Bayesian structure learning
AU - Edwards, Richard E.
AU - New, Joshua R.
AU - Parker, Lynne E.
PY - 2013
Y1 - 2013
N2 - Many key building design policies are made using sophisticated computer simulations such as EnergyPlus (E+), the DOE flagship whole-building energy simulation engine. E+ and other sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. Currently, these problems are addressed by having an engineer manually calibrate simulation parameters to real world data or using algorithmic optimization methods to adjust the building parameters. However, some simulations engines, like E+, are computationally expensive, which makes repeatedly evaluating the simulation engine costly. This work explores addressing this issue by automatically discovering the simulation's internal input and output dependencies from ∼20 Gigabytes of E+ simulation data, future extensions will use ∼200 Terabytes of E+ simulation data. The model is validated by inferring building parameters for E+ simulations with ground truth building parameters. Our results indicate that the model accurately represents parameter means with some deviation from the means, but does not support inferring parameter values that exist on the distribution's tail.
AB - Many key building design policies are made using sophisticated computer simulations such as EnergyPlus (E+), the DOE flagship whole-building energy simulation engine. E+ and other sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. Currently, these problems are addressed by having an engineer manually calibrate simulation parameters to real world data or using algorithmic optimization methods to adjust the building parameters. However, some simulations engines, like E+, are computationally expensive, which makes repeatedly evaluating the simulation engine costly. This work explores addressing this issue by automatically discovering the simulation's internal input and output dependencies from ∼20 Gigabytes of E+ simulation data, future extensions will use ∼200 Terabytes of E+ simulation data. The model is validated by inferring building parameters for E+ simulations with ground truth building parameters. Our results indicate that the model accurately represents parameter means with some deviation from the means, but does not support inferring parameter values that exist on the distribution's tail.
KW - Big Data
KW - Probabilistic Inference
KW - Structure Learning
UR - http://www.scopus.com/inward/record.url?scp=84890583221&partnerID=8YFLogxK
U2 - 10.1145/2501221.2501226
DO - 10.1145/2501221.2501226
M3 - Conference contribution
AN - SCOPUS:84890583221
SN - 9781450323246
T3 - Proc. of 2nd Int. Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine 2013 - Held in Conj. with SIGKDD 2013 Conf.
SP - 31
EP - 38
BT - Proc. of 2nd Int. Workshop on Big Data, Streams and Heterogeneous Source Mining
PB - Association for Computing Machinery
T2 - 2nd Int. Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine 2013 - Held in Conj. with SIGKDD 2013 Conf.
Y2 - 11 August 2013 through 11 August 2013
ER -