Time-of-Day electricity rates: successful implementation is complicated
Bibliographic record
Abstract
On the face of it, implementing a time-of-day electricity rate program should be straightforward: develop a rate structure (based on avoiding electricity consumption at the times when the load on the grid is highest), and instruct people that their usage at peak times will be billed at a higher rate than at other times. Looking at billing as a simple case of applied behaviour analysis, it ought to be the case that if people reduce their use at peak times their bills will drop, and this should reward the behaviour. A common energy efficiency instruction to set back one’s thermostat overnight does not guarantee a reduction in peak energy demand in dwellings that use electricity for space heating; some technologies work best with a ‘set it and forget it’ strategy, but this instruction is contrary to long-held habits. Using data from a study of >300 participants in a time-of-day field trial in Atlantic Canada, this presentation will demonstrate that successful behaviour change in electricity use also depends on the behaviour of the technologies available to the participants, regardless of the motivation of the individuals. Energy behaviour program design needs a collaborative approach between engineers and psychologists for best results.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".