Peak Load Curtailment in a Smart Grid Via Fuzzy System Approach
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it