Designing Persuasive Technology to Reduce Peak Electricity Demand in Ontario Homes
Bibliographic record
Abstract
When it comes to environmental sustainability, the time that electricity is consumed matters. For example, using an air conditioner on a hot summer afternoon as the power grid is strained necessitates the use of more polluting sources to meet demand. There are a number of ways to target a reduction in peak demand: better electricity storage technology, for one, has the potential to level out these peaks. In the meantime, electrical utilities aim to incentivize a reduction in demand from households at these times through programs such as Time-of-Use Pricing, and critical peak demand response programs, such as peaksaver in Ontario. However, the effectiveness of these programs has been limited. \nIn this thesis, we adopted the lens of persuasive technology to improve and support these programs, in order to encourage a reduction in electricity consumption in households at peak load times. To accomplish this, we conducted 18 interviews to examine the practices of households in response to these programs, and learn how they can be improved at the individual level. We found that Time-of-Use pricing encourages shifting some electricity demand, but only when it is convenient. We also found that while potentially effective at a larger scale, the peaksaver program in its current form is unattractive to participants. \nWe then analyzed our findings using existing behaviour change models, including Fogg’s Behaviour Model for Persuasive Design. Using the three aspects of the model – motivation, ability and triggers - we identified where the existing programs are lacking and developed design implications for the design of persuasive technology to support reducing electricity consumption at peak load times. Finally, we designed a smartphone application based on these design implications, and conducted a preliminary evaluation in order to begin to assess their validity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 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".