Energy Not-Served-Based Method for Assessing Smart Grid Functions in Residential Loads
Bibliographic record
Abstract
This article presents a method for evaluating smart grid functions that are implemented to operate residential loads. The proposed assessment method is developed based on the energy not-served (ENS) determined at a point-of-supply feeding residential loads. Smart grid functions can operate energy storage appliances (household water heaters, air conditioners, and heating units) to store thermal energy during the daily off-peak-demand hours. The stored thermal energy is discharged during the daily peak-demand hours, thus reducing the power demands of residential loads. The differences in daily energy demands created by smart grid functions can provide an accurate assessment of the effectiveness of smart grid functions. The ENS-based method is tested for 200 residential households fed from four distribution transformers, and are operated by smart grid functions. In these tests, smart grid functions are implemented by the peak-demand management, direct load control, and demand response. Test results demonstrate the accuracy and simplicity of the ENS-based method to assess smart grid functions in terms of the ability to reduce the power demands of residential loads during peak-demand hours.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".