Designing effective demand response: A review of behavioral insights, consumer engagement, and operational strategies in energy systems
Bibliographic record
Abstract
Demand response (DR) programs help electricity systems balance supply and demand by encouraging consumers to adjust their usage in response to price signals or incentives. As population growth, urbanization, and the electrification of transportation and industry continue to increase energy needs, flexible and effective DR programs have become increasingly important. However, consumer participation remains difficult to secure because individuals are influenced by a range of cognitive factors that shape how they perceive and respond to DR incentives. In this paper, we identify five broad categories of cognitive influences that systematically affect consumer decision-making in DR settings. For each category, we provide representative examples from DR programs and review the relevant literature. We then develop a conceptual framework linking consumer engagement, cognitive influences, and socioeconomic factors, and propose unified strategies for integrating behavioral interventions at each stage of the DR engagement process. These strategies aim not only to strengthen consumer participation but also to enhance the overall effectiveness of DR programs. By deepening our understanding of how behavioral factors shape consumer responses, this review offers actionable insights for practitioners, policymakers, and researchers seeking to design DR interventions that improve energy system performance while promoting efficiency and sustainable consumption habits.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".