Impact of measured data frequency on commercial building energy model calibration for retrofit analysis
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".