Probabilistic commercial load profiles at different climate zones
Bibliographic record
Abstract
This paper presents a numerical representation for commercial load profiles based on different climate zones. The load profiles of 16 commercial buildings located in 935 cities representing 50 States in the United States (U.S.) are clustered using k-means considering the geographic coordinates and the time zones. The geographic coordinate works as a local criterion to assign a climate zone for cities within the state, the time zone acts as a regional criterion to group cities with the same climate zone in different states based on their time zones. The clusters are evaluated using both external and internal validity indices. A total of 16 annual load profiles are used as representative for 16 different climate zones for each commercial building in this paper. Unlike the prevalent illustration for the commercial load profiles in the literature using graph representation, the obtained profiles in this study are numerically presented. This paper contributes to the literature by proposing a numerical representation for commercial load profiles at 16 climate zones, which are in turn can be used widely in smart grid application.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".