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A Bottom-Up Method to Calibrate Building Energy Models Using Building Automation System (BAS) Trend Data


A Bottom-Up Method to Calibrate Building Energy Models Using Building Automation System (BAS) Trend Data

Zibin, Nicholas (2014) A Bottom-Up Method to Calibrate Building Energy Models Using Building Automation System (BAS) Trend Data. Masters thesis, Concordia University.

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Zibin_Thesis20141217.pdf - Accepted Version
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Ongoing commissioning based on calibrated building energy models is one of the most promising means to improve the energy performance of existing buildings. Many calibration methods in the literature relied on whole-building utility data to calibrate building energy models. Recent studies revealed that only using this data could result in offsetting errors occurring at sub-utility levels. To reduce offsetting errors, a new bottom-up calibration method was developed where the zone, system, plant, and whole-building models are sequentially calibrated.

The number of candidate measurement points required for bottom-up calibration is large. Fortunately, building automation systems (BASs), common in many commercial/institutional buildings, can provide some of the required data. To reduce the time for BAS trend data analysis, a new proof-of-concept prototype, called the Automatic Assisted Calibration Tool (AACT) was developed and tested to couple trend data with calibrated simulation by automatically generating inputs to update an eQUEST input file.

This thesis documents the use of the AACT and bottom-up method to calibrate an eQUEST energy model of a case study building focusing on the zone and system level models. Using inputs generated from trend data and calibrating the zone level first often yielded a calibrated system level model. Limitations representing measured physical performance in eQUEST were encountered causing unintended offsetting errors occurring at the sub-utility levels. Overall, the use of BAS trend data with a bottom-up method can reduce offsetting errors during calibrated simulation.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Concordia University > Research Units > Centre for Zero Energy Building Studies
Item Type:Thesis (Masters)
Authors:Zibin, Nicholas
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:18 December 2014
Thesis Supervisor(s):Zmeureanu, R.G. and Love, J.A.
Keywords:Building automation system (BAS), trend data, calibration, energy, HVAC, eQUEST
ID Code:979581
Deposited On:09 Jul 2015 16:35
Last Modified:18 Jan 2018 17:49
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