Impact of measured data frequency on commercial building energy model calibration for retrofit analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it