Scalable tuning of building models to hourly data
- Jacksonville State Univ., Jacksonville, AL (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for small projects, imperfect, non-repeatable, non-transferable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and cheap/ubiquitous sensors are beginning to make available today. A scalable, automated methodology is needed to quickly and intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The "Autotune'' project is a novel, model-agnostic methodology which leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Furthermore, accuracy metrics are provided which quantify model error to measured data for either monthly or hourly electrical usage from a highly-instrumented, emulated-occupancy research home.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Organization:
- USDOE Office of Science (SC); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- Grant/Contract Number:
- AC05-00OR22725; CEBT105; BT0201000; DEAC05-00OR22725
- OSTI ID:
- 1185507
- Alternate ID(s):
- OSTI ID: 1247854
- Journal Information:
- Energy (Oxford), Vol. 84; ISSN 0360-5442
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Building simulation: Ten challenges
|
journal | April 2018 |
Similar Records
Evaluation of “Autotune” calibration against manual calibration of building energy models
Quality Control Methods for Advanced Metering Infrastructure Data