Using a Cluster-Based Method for Controlling the Aggregated Power Consumption of Air Conditioners in a Demand-Side Management Program
Bibliographic record
Abstract
Thermo-storage units such as air conditioners (AC) or space heaters offer significant potential for the demand-side regulation and balancing the consumption with the generation. This makes them attractive resources to mitigate the fluctuation and intermittency of the renewable resources, such as solar. This paper presents a control strategy to adjust the thermostat set points of the air conditioners in a way that the aggregated power consumptions of the ACs would follow a desired trajectory, while maintaining customers' comfort through the level of thermostat set points. We used a clustering technique over the time-series power consumptions of individual ACs to find similar patterns. Similar power profiles maybe indicative of similar room temperatures; and hence, adjusting the set point of similar ACs may better maintain customers comfort. Using a mathematical model for the air conditioners, a simulator was built to assess the performance of this controller in different scenarios where the effect of changes in the ambient temperature was also studied. Our results show that this system can follow the desired aggregated power within 10 minutes, which can be used for ancillary services, such as 10-minutes spinning reserve, in power electric systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".