The development of a fuzzy neural system for load forecasting
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Bibliographic record
Abstract
In order to design an aggregate domestic load control system, a controller requires accurate predictions of load curves to make decisions about which loads should be connected to the grid. This paper presents a 24-hour load forecaster to be used by the controller. The forecaster will employ an Artificial Neural Network (ANN) structure with one input provided by a fuzzy weather controller. The use of fuzzy logic will enhance the performance of the system as well as make it more transparent and adaptable. A unique method is introduced to efficiently incorporate a larger number of inputs into the fuzzy controller without the problem of having an unmanageable rule base. The results show that the fuzzy neural system performs better than the artificial neural network load forecaster with further gains possible by fine-tuning the fuzzy logic block.
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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 | 0.000 | 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) | 0.000 | 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 it