Chillers + Lighting + TES Why CFC Chiller Replacement
Bibliographic record
Abstract
As we approach the ten-year anniversary of the chlorofl uorocarbon (CFC) ban that took effect in 1996, only 57 % of the estimated 85,486 large tonnage CFC chillers in the U.S. and Canada have been replaced or converted. According to the estimates from the Air-Condi-tioning and Refrigeration Institute (ARI)1 and the Heating, Refrigeration and Air-Conditioning Institute of Canada (HRAI),2 by the end of 2004, approximately 37,000 CFC chillers still operated in North America. Giuliano Todesco is an energy systems engineer-ing technologist with Jacques Whitford in Ottawa, ON, Canada. By Giuliano Todesco, Member ASHRAE $3.4 billion to $4.8 billion. In addition, the cooling and lighting loads in these buildings contribute an estimated 3,600 to 9,200 MW to the summer peak demand of North American utilities. The electricity consumption and peak electrical demand can be reduced signifi-cantly by replacing the remaining CFC chillers with new effi cient plants. The performance of chillers has improved signifi cantly in the last 12 years compared to chillers manufactured in the 1970s and 1980s. Additional electricity energy savings are also possible with cooling These CFC chillers serve an estimated 3.4 billion to 4.7 billion ft (315 million to 440 million m) of commercial fl oor space with a total electricity consump-tion of 49,000 to 66,000 GWh/year, and an annual electricity operating cost of
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".