Bibliographic record
Abstract
Test and Rating procedure was adopted by the US Department of Energy in 1979 to provide consumers with a relative indication of seasonal air conditioner efficiency. A quarter century later, experience suggests that SEER is not an adequate measure or metric for meeting the regional needs of homeowners and utilities. For instance, in hot-dry climates SEER does not provide an adequate prediction of seasonal energy costs for consumers or peak demand impacts on electric utilities. Similarly, in hot, humid climates, SEER does not consider the dehumidification performance of a cooling system, which is critically important to customers. New testing and rating procedures may be required to address these market changes. This paper evaluates one potential improvement to the SEER procedure: a regional SEER that could incorporate weather data to better predict seasonal performance. The bin-calculated SEER is calculated for various TMY2 locations and compared to the seasonal efficiency predicted by a detailed hourly simulation model. The nominal SEER for the modeled air conditioning unit was 11.7 Btu/Wh. The seasonal efficiencies predicted by the simulation model for 19 US locations ranged from 10.3 to 11.9 Btu/Wh. The bin-calculated SEER that uses location-specific bin data accounts for about half of the SEER variation. The remaining portion of the SEER variation is due to different humidity conditions entering the indoor coil in various climates. The concept of the regional SEER would provide consumers with a better indication of the energy efficiency and could be calculated using the same set of test data that manufacturers now collect on their systems (i.e., no additional test burden for manufacturers).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.469 | 0.268 |
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".