A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Impact of measured data frequency on commercial building energy model calibration for retrofit analysis
Developing an accurate energy model remains challenging because of the numerous parameters that define a building’s performance and the difficulty of the measuring them. Automated calibration using measured data can be used to develop an accurate energy model. This paper investigates the impact of the monitored data frequency (hourly vs. monthly) on the calibration results and retrofit analysis. A 11-storey government office building located in Ontario, Canada was selected as a case study to demonstrate the proposed methodology. Sensitivity analysis using a variance-based method was conducted to select the calibration parameters. The results of optimization calibration using two measured data frequencies demonstrated that monthly calibrations were unable to reflect actual operation conditions of a case-study building, thus indicating a necessity for hourly calibrations. Although the monthly calibrated model had the minimum average value of the CV(RMSE) of monthly energy consumption (7.4%), the CV(RMSE) of the hourly heating usage for that model was about 38.2%. Implementation of several energy saving measures on both calibrated models revealed that the resolution of measured data can significantly affect the estimated impact of energy saving measures.
Impact of measured data frequency on commercial building energy model calibration for retrofit analysis
Developing an accurate energy model remains challenging because of the numerous parameters that define a building’s performance and the difficulty of the measuring them. Automated calibration using measured data can be used to develop an accurate energy model. This paper investigates the impact of the monitored data frequency (hourly vs. monthly) on the calibration results and retrofit analysis. A 11-storey government office building located in Ontario, Canada was selected as a case study to demonstrate the proposed methodology. Sensitivity analysis using a variance-based method was conducted to select the calibration parameters. The results of optimization calibration using two measured data frequencies demonstrated that monthly calibrations were unable to reflect actual operation conditions of a case-study building, thus indicating a necessity for hourly calibrations. Although the monthly calibrated model had the minimum average value of the CV(RMSE) of monthly energy consumption (7.4%), the CV(RMSE) of the hourly heating usage for that model was about 38.2%. Implementation of several energy saving measures on both calibrated models revealed that the resolution of measured data can significantly affect the estimated impact of energy saving measures.
Impact of measured data frequency on commercial building energy model calibration for retrofit analysis
Derakhti, Mohammad (author) / O’Brien, William (author) / Bucking, Scott (author)
Science and Technology for the Built Environment ; 28 ; 628-644
2022-05-27
17 pages
Article (Journal)
Electronic Resource
Unknown
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