Real-World Implementation of Residential Thermostat Control for DR
Bibliographic record
Abstract
Traditionally, operators relied on the right mix of generators at their disposal for grid services, but now with two-way communication enabled by the smart grid, demand-response (DR) becomes another option for the electric utility to deploy control strategies shaping the demand profile. DR strategies can tap into the demand flexibility potential of large populations of residential loads to shift electricity use across hours of the day while maintaining the same comfort level. This paper presents the results from applying a DR strategy to the electric baseboards of eleven homes over a two-month period during Winter 2016/17. The DR strategy applies setpoint modulation to baseboard heaters via smart thermostats to store thermal energy prior to peak hours and then uses this stored energy to reduce demand. It is found that demand reductions of 36% and 24% can be achieved during morning and afternoon peaks, respectively, with a small daily reduction in energy consumption. DR holds significant potential for peak shaving where residential heating accounts for an important share of the utility's demand and can be had with little to no user discomfort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".