Towards a smart and sustainable residential energy culture: assessing participant feedback from a long-term smart grid pilot project
Bibliographic record
Abstract
Smart grid tools (e.g., individualized disaggregated data, goal setting, and behavioural suggestions/feedback) increase opportunities to reduce or shift residential electricity consumption, but can they shape residential energy culture? And what underlying factors influence this shift? Insights are identified from a qualitative analysis of a 3-year residential smart grid project in a suburb of Toronto, Canada. Interviews evaluated whether participants experienced changes in their energy culture and identified underlying factors. In particular, the impacts of the project tools on participants’ norms (attitudes and awareness towards energy management), material culture (technical changes) and energy practices (conservation/peak shifting actions) were assessed, and motivations and barriers towards energy management were identified. The effectiveness of engagement mechanisms (i.e., web portal, reminder emails, webinars, incentivized control programme, and weekly electricity reports) was also evaluated. By examining detailed qualitative feedback following a multi-year suburban smart grid project in Ontario this study aims to (1) assess the changes in energy culture over the duration of the 3-year project and to (2) assess the underlying factors influencing household energy consumption and a smart residential energy culture. Findings from the interview were compared to the results of an initial project survey to identify longer-term influences on energy culture. Increases in self-reported awareness and practices were accounted for, with the web portal and individualized weekly feedback email reported most frequently as causes of change. While increased awareness was obtained, participants needed additional guidance to make substantial changes. Although participants were financially motivated, norms of lifestyle and convenience, as well as competing household values of energy management were the largest barriers to home energy management. This study showcases challenges for engaging homeowners with home energy management technologies due to norms as well as competing household interests. Nuanced findings as an outcome of this study framed around energy cultures can influence future studies on smart grid engagement and consumer behaviour with larger samples sizes. In particular, future studies can further investigate the motivations and barriers surrounding residential energy cultures, how to engage different ‘cultures of consumption’ within households, and elements to effectively educate consumers beyond disaggregated feedback.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".