Peak Load Curtailment in a Smart Grid Via Fuzzy System Approach
Bibliographic record
Abstract
Among many significant smart grid initiatives and challenges considered by many utilities and within the research community, are those associated with the energy management and conservation, in particular the management of energy demand during peak load periods. In this paper, a novel method for peak load curtailment by using a fuzzy system approach is presented. The proposed method is based on the application of fuzzy logic principles for peak load curtailment in a smart grid environment. The inputs to the system are the utility peak load data consisting of many energy demand scenarios, and the outputs are the necessary demand response power reductions required for the load curtailment during the peak load periods. The proposed method considers different peak load profiles and power consumption sources for multiple city regions. Furthermore, it is adaptable for use in many scenarios, such as those encompassing many input sources of power consumption with diverse input parameters of control (i.e., temperature offsets, duty cycle control, etc.) within numerous city regions. Thus, it can be applied to multiple output variables of control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".