Development of a clustering-based morning start time estimation algorithm for space heating and cooling
Bibliographic record
Abstract
The morning start time of heating and cooling equipment plays an important role in the energy and comfort performance of buildings. Existing algorithms to guide this decision require either many data types with a consistent labelling nomenclature or a detailed calibrated model. In this paper, a model-free clustering-based morning start time estimation algorithm is put forward. The algorithm inputs only four types of data: indoor and outdoor temperatures, and heating and cooling energy use, and does not require any information regarding the location of the temperature sensors. The algorithm consists of four steps. The first one employs clustering to form groups of zones with a similar temperature response. The second one searches for inflection points to identify cluster temperature change rates during morning start-up periods. The third one determines the start time based on previous morning start-up temperature change rates. The last one estimates the energy savings potential by using bivariate change point models. The algorithm was developed by using a dataset from a large office building. Through hierarchical clustering, the data from 142 temperature sensors were consolidated to only seven clusters. The median morning start-up temperature change rates in individual clusters were between 0.3°C/h and 0.8°C/h for heating, and between -0.5°C/h and -1.2°C/h for cooling. The savings potential by tuning daily start times based on this information was estimated as 3% and 7% for heating and cooling, respectively.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".