Bibliographic record
Abstract
The deployment of advanced metering infrastructure, most notably in the residential electricity sector, and the development of energy monitoring technology, has enabled and justified the exploration of more sophisticated systems to energy management in the home. Previously, energy usage feedback has been shown to help householders to learn and to adapt usage behaviour. However, these systems are still in their infancy and exploration of householder interest in them, and their designs, is needed to assist in the diffusion of this innovation. \nThe effectiveness of feedback has been found to be enhanced when it is shown relative to a conservation goal. Additionally, disaggregated feedback has been found desirable to householders and has helped them to learn. However, little is understood about how disaggregated home energy feedback relative to a goal can be designed to help householders to conserve. This study explored interest in home energy goal-setting through a web-based survey of Ontario householders. \nInferential statistics showed, with 95% confidence, that 35% to 37% of urban Ontario homeowners with post-secondary education would be strongly interested in setting home energy goals – and 29% to 31% would be willing-to-pay at least $6 per month for home energy goal-setting technology. Both financial and environmental reasons were often cited by respondents for their interest. Interest in home energy goal-setting did not relate to experience or interest in setting ‘non-energy’ goals but was significantly and positively associated with: (1) awareness in the environmental impact of energy usage, (2) pro-sustainability attitudes and behaviours, (3) desire to reduce usage, (4) desire to learn, and (5) motivations to help society. Householders preferred web-based feedback to other mediums such as in-home displays or bills. They also identified goal-based appliance-specific feedback as desirable but found the graphical presentation of multiple appliances on one page confusing, and instead preferred numerical presentations. Both extrinsic and intrinsic benefits to home energy goal-setting were supported by respondents and the most cited barrier to goal-setting was that it takes a lot of time. However, it is expected that a home energy monitoring system would help mitigate this potential barrier since progress would be tracked and reported automatically.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".