Unsupervised energy disaggregation using time series decomposition for commercial buildings
Bibliographic record
Abstract
"We can't manage what we don't measure." Understanding energy flow and end-uses within a building is essential for energy management. Although submetering is very important, many buildings still do not have adequate submetering for their major end-uses due to cost and practical issues. Energy disaggregation approaches can break down the bulkmeter energy signal into specific end-uses to gain insight into consumption patterns. With high-quality building automation system (BAS) trend data, unmeasured energy flow can be captured at high accuracy using disaggregation techniques. However, it remains an untackled challenge to disaggregate end-uses without high-resolution, reliable BAS trend data. This paper explores the time-series decomposition-based method to disaggregate major energy end-uses without BAS trend data. The proposed decomposition model uses data from an office building in Ottawa, Canada, to break down the total energy use into three major end-uses: lighting and plug loads and cooling and heating energy use. The disaggregation result was then compared with actual submeter data for validation purposes. The proposed method's promising performance points to its potential application in quick and low-cost auditing of commercial buildings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".