Energy use of US residential refrigerators and freezers: function derivation based on household and climate characteristics
Bibliographic record
Abstract
Field-metered energy use data for 1,467 refrigerators and 185 freezers from seven studies conducted between 1992 and 2010 were used to calculate usage adjustment factors (UAFs), defined as the ratio of measured to tested annual energy use.Multiple regressions of UAFs against several household and climate variables were then performed to obtain separate predictive functions for primary (mostused) refrigerators, secondary (second most-used) refrigerators, and freezers, and residual differences between observed and modeled UAFs were fit to log normal distributions.These UAF functions were used to project energy use in the more than 4,000 households in the 2005 Residential Energy Consumption Survey, a statistical representation of US homes.These energy use projections formed the basis of calculating lifecycle energy savings for more efficient refrigerators and freezers, as well as national energy and cost savings.Results were compared with previous published work by the Department of Energy, demonstrating how UAFs impact energy and cost savings.Such an approach could be further improved with additional data and adapted for other appliances in future analyses. Keywords Refrigerator •
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".