A pattern-based automated approach to building energy model calibration

TitleA pattern-based automated approach to building energy model calibration
Publication TypeJournal Article
Year of Publication2015
AuthorsSun, Kaiyu, Tianzhen Hong, Sarah C. Taylor-Lange, and Mary Ann Piette
Abstract

Building model calibration is critical in bringing simulated energy use closer to the actual consumption.
This paper presents a novel, automated model calibration approach that uses logic linking parameter tuning
with bias pattern recognition to overcome some of the disadvantages associated with traditional calibration
processes. The pattern-based process contains four key steps: (1) running the original precalibrated
energy model to obtain monthly simulated electricity and gas use; (2) establishing a pattern
bias, either Universal or Seasonal Bias, by comparing load shape patterns of simulated and actual monthly
energy use; (3) using programmed logic to select which parameter to tune first based on bias pattern,
weather and input parameter interactions; and (4) automatically tuning the calibration parameters
and checking the progress using pattern-fit criteria. The automated calibration algorithm was implemented
in the Commercial Building Energy Saver, a web-based building energy retrofit analysis toolkit.
The proof of success of the methodology was demonstrated using a case study of an office building
located in San Francisco. The case study inputs included the monthly electricity bill, monthly gas bill,
original building model and weather data with outputs resulting in a calibrated model that more closely
matched that of the actual building energy use profile. The novelty of the developed calibration methodology
lies in linking parameter tuning with the underlying logic associated with bias pattern identification.
Although there are some limitations to this approach, the pattern-based automated calibration
methodology can be universally adopted as an alternative to manual or hierarchical calibration
approaches.

LBNL Report Number

LBNL-1004495