Selection Of Energy Conservation Measures in a Large Office Building using Decision Models under Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Energy analysis programs are often used for predicting the impact of energy-related building retrofit on the annual energy consumption and cost. However, due to many uncertainties involved in the development of the input file, the energy savings are often overestimated or under estimated. In order to increase the accuracy of predictions, this paper proposes the use of decision models under uncertainty, to determine the most profitable alternative, given the possible errors which may be introduced by the user in the input file. This approach is applied to a large existing office building in Montreal. The predicted annual cost savings, the payback period and the benefit-cost ratio are calculated using the building energy analysis software MICR0-D0E2.1E. The results are then analyzed using different criteria and, finally, some energy conservation measures are selected.
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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 | 0.001 | 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