Real-Time Feedback and Residential Electricity Consumption: The Newfoundland and Labrador Pilot
Bibliographic record
Abstract
A pilot study was undertaken in Newfoundland and Labrador to determine whether provision of a real-time feedback device is sufficient to provide residential customers with the information needed to reduce their electricity consumption. A panel based econometric methodology, which controlled for such factors as weather, appliance and housing stock, and demographic determinants influencing electricity consumption, was used to quantify the impacts of the realtime monitor in reducing energy (kWh) use. The study also provided some important insights about socio-economic factors that influence conservation responsiveness, a feature that may assist in developing targeted energy efficiency programs. For example, the electric water heating households showed a higher savings than non-electric water heating households. While positive attitudes toward conservation significantly increase the reduction in electricity when using the real-time monitor, seniors, in their employment of the real-time monitor, do not conserve as much. Overall, the aggregate reduction in electricity consumption (kWh) across the study sample was 18.1%. The paper describes the experimental design, the data collection, the evaluation model, the conservation results, and customers' attitudes and perceptions regarding the real-time monitor.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".