Decision-Making Impacts on Energy Consumption Display Design
Bibliographic record
Abstract
Policy makers are considering smart grid technologies such as energy consumption displays (ECD) as a means of changing the behavior of energy consumers. This article provides a review of consumer energy management behavior, presents decision-making models, and considers consumer–technology interface (CTI) display guidelines in ECD design. Existing research finds the underlying drivers of energy management behavior to be existing motivators and sociodemographics. An analysis of ECD research demonstrates a preference for an energy consumption graphic, consumption rates in monetary units, the ability to provide detailed and historical information at the appliance level, and the avoidance of unknown parameters. Integrating CTI guidelines with ECD preferences and decision-making approaches yields two ECD models. The article presents two prototype ECD models that serve different decision-making approaches. The first ECD prototype is designed to serve routine or predisposition decision makers, presenting summary information and employing colors and graphics. The second ECD prototype is designed to serve rational decision makers, presenting detailed appliance-level information, goal-setting capabilities, and the ability to produce detailed history. Further research is recommended to establish decision-making preferences, predisposition elements, threshold parameters, and ECD prototype testing by region.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".