A method for distinguishing appliance, lighting and plug load profiles from electricity ‘smart meter’ datasets
Bibliographic record
Abstract
In the Canadian residential sector, the end-uses of appliances, lighting, and plug loads (ALP) account for 16% of total end-use energy consumption. In an effort to reduce the impacts of this energy consumption, electricity technologies such as solar photovoltaics and smart appliances are being adopted. Evaluation of their performance requires an understanding of residential electricity use patterns. Building simulation tools can estimate the time-step performance of such technologies, but require accurate and representative ALP electricity profiles as an input. Sub-metered datasets lack in quantity and thus overall representativeness of the sector. Meanwhile, large, representative datasets are becoming available through electricity smart-metering programs, but usually consist only of whole-house electricity load and lack summary household characteristics (e.g. occupancy, floor space, appliance descriptions). However, homes which are not electrically heated (space, water) or cooled may function as ALP load profiles for simulation. This research addresses these loads with a new method of distinguishing non-electrically heated and cooled homes from a broad dataset of whole-house profiles. The method originates from a comparison of two electricity load datasets: (i) “smart-meter” 15-min time-step whole-house data for 160 homes spanning three years, and (ii) “sub-metered” 1-min time-step data for 23 residential homes. This comparison also speaks to the usefulness of whole-house electricity smart-meter information to building performance simulation.
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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".