Investigating the full potential of demand response programs using granular occupancy schedules
Bibliographic record
Abstract
Demand Response (DR) programs are critical for enhancing grid reliability by incentivizing users to adjust their energy consumption during peak times. Traditionally, residential DR programs have focused on managing heating and cooling demands. However, there is substantial potential to expand these programs to encompass other energy-intensive activities, such as cooking, dishwashing, clothes washing/drying, and the use of other appliances. This paper explores the simulation of residential end-use patterns to develop effective DR programs that can lower energy costs for consumers and reduce the environmental impact of peak demand events. The study leverages granular occupancy schedules derived from the Donate Your Data (DYD) dataset by Ecobee smart thermostats, providing a more accurate representation of occupancy patterns in North American residential buildings. These schedules are augmented with insights from nationwide time-use surveys (TUS), which detail the temporal distribution of various energy-intensive activities within households. Previous analyses have delineated patterns of energy end-use, serving as a foundation for this study.This paper simulates residential occupancy patterns across a representative cluster of 10 households, capturing the diversity observed within the DYD dataset. The study aims to 1) compare energy consumption differences between standard schedules and the developed occupancy schedules, and 2) quantify the potential impact of tailored DR programs or direct load control on reducing peak demand.The methodology involves a rule-based framework to translate motion detection data from smart thermostats into whole-building occupancy schedules, accounting for limitations like infrequent or false motion triggers. Simulations are conducted using EnergyPlus, incorporating Canadian residential archetypes. The impact of using these occupancy schedules (vis-à-vis standard schedules) on total household energy use, particularly concerning heating, cooling, lighting, and appliance usage, is analyzed. The study further estimates the potential for DR by shifting energy-intensive activities from peak demand periods to other times when the household is occupied, as indicated by the developed schedules. The significance of this research lies in its ability to inform more accurate and effective DR strategies, leading to optimized energy usage and reduced strain on the power grid during peak times. By providing a clearer understanding of residential energy consumption patterns, the findings can help policymakers and utility companies design targeted interventions that enhance grid stability, lower energy costs, and contribute to environmental sustainability.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".