Take the Load Off: Effort and Technology as Determinants of Electricity Demand Response
Bibliographic record
Abstract
As electricity systems transition toward more variable renewable energy, flexible demand has emerged as a critical tool for grid management.Yet a fundamental question remains: are emerging smart technologies sufficient to unlock demand response, or does human behavior remain the critical barrier?Our field experiment examines this question through a novel approach that individually randomizes peak event timing for each participating household, allowing us to leverage both within-subject and between-subject variation.We compare the response to "peak events" on electricity consumption for households equipped with three distinct demand response programs: a fully automated system requiring no action; app-enabled smart devices requiring minimal effort; and traditional manual adjustments.The results are striking-households with passive, automated responses reduced consumption five times more than those required to take any action at all, even when the burden is greatly reduced via smart technology.The provision of enabling technologies alone made no difference in households' responsiveness, as compared to a fully manual setting, when active participation was still required.These findings reveal that the opportunity cost of time and effort-not technology limitations-may be the fundamental obstacle to unlocking electricity demand flexibility.To achieve its full potential, "smart home" technologies need to incorporate these behavioral realities as barriers to responsiveness.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".