Supercomputer assisted generation of machine learning agents for the calibration of building energy models

ACM International Conference Proceeding Series

Volumn , Issue , 2013, Pages

  Sanyal, Jibonananda ^a   New, Joshua ^a   Edwards, Richard ^b  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

^b University of Tennessee   (United States)

Author keywords

Big data; Building energy modeling; Calibration; Machine learning; Parametric ensemble; Supercomputer

Indexed keywords

BIG DATUM; BUILDING ENERGY MODEL; BUILDING STOCKS; COMPUTATIONAL PROBLEM; DEPARTMENT OF ENERGY; MACHINE LEARNING AGENTS; PARAMETRIC ENSEMBLE; PARAMETRIC SPACES;

BOUNDARY ELEMENT METHOD; CALIBRATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; SUPERCOMPUTERS;

BUILDINGS;

EID: 84882441265     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2484762.2484818     Document Type: Conference Paper

References (19)

1. Big Ladder Software. Epx, a modified version of energyplus v. 7, 2013.
2. Briggs, R., Lucas, R., and Taylor, Z. Climate classification for building energy codes and standards: Part 1-development process. ASHRAE Transactions 109, 1 (2003), 109-121.
3. Briggs, R., Lucas, R., and Taylor, Z. Climate classification for building energy codes and standards: Part 2-zone definitions, maps, and comparisons. ASHRAE Transactions 109, 1 (2003), 122-130.
4. Castello, C. C., and New, J. R. Autonomous correction of sensor data applied to building technologies utilizing filtering methods. Fourth ACM International Conference on Future Energy Systems (2012).
5. Castello, C. C., and New, J. R. Autonomous correction of sensor data applied to building technologies utilizing statistical processing methods. 2nd Energy Informatics Conference (2012).
6. Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., Liu, B., Halverson, M., Winiarski, D., Rosenberg, M., et al. US Department of Energy Commercial Reference Building Models of the National Building Stock.
7. DeWit, M. Uncertainty in predictions of thermal comfort in buildings. PhD thesis, Technische Universiteit Deft, Netherlands, 2001.
8. Edwards, R. E., New, J. R., and Parker, L. Predicting future hourly residential electrical consumption: A machine learning case study. Buildings and Energy (2012).
9. Edwards, R. E., New, J. R., and Parker, L. Constructing large scale energyplus surrogates from large data. Buildings and Energy (2014).
10. Edwards, R. E., and Parker, L. Sensor-based energy modeling final report fy2012. Tech. rep., Oak Ridge National Laboratory, TN, 2012.
11. Edwards, R. E., and Parker, L. Mlsuite final report fy2012. Tech. rep., Oak Ridge National Laboratory, TN, 2013.
12. Field, K., Deru, M., and Studer, D. Using DOE Commercial Reference Buildings for Simulation Studies, 2010.
13. Garrett, A., and New, J. R. Evolutionary tuning of building model parameters. Tech. rep., Oak Ridge National Laboratory, TN, 2012.
14. Garrett, A., New, J. R., and Chandler, T. Evolutionary tuning of building models to monthly electrical consumption. ASHRAE Annual Conference (2014).
15. IECC 2009 and ASHRAE 90.1-2007. Energy Code Climate Zones.
16. National Institute for Computational Sciences. Supercomputer-assisted calibration methodology enhances accuracy of energy models, 2013.
17. New, J. R., Sanyal, J., Bhandari, M. S., and Shrestha, S. S. Autotune e+ building energy models. 5th National SimBuild of IBPSA-USA (2012).
18. U.S. Dept. of Energy. Building Energy Data Book. D&R International, Ltd., 2010.
19. U.S. Dept. of Energy. Building Energy Data Book. D&R International, Ltd., 2011.