Singular Spectrum Analysis and Neural Network to Forecast Demand in Industry
Why this work is in the frame
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Bibliographic record
Abstract
The relationship between energy consumption and supply is a primary factor in the planning and operation of power systems. Brazil is experiencing major problems with the energy crisis into which it was placed. The lack of investment in energy supply is one of the determining factors. During the 1980s, these investments cost $10 billion on average every year. In recent years, however, half reduced these investments. This paper proposes a method for demand forecasting based on the Singular Spectrum Analysis (SSA) and neural network. The methods are to be used by large power utility's customers and to be implemented in real-time and prevents peaks from surpassing the contracted power demand with the utility. It can be applied as an auxiliary tool for management of electrical power demand in industrial plants. The effectiveness of the method is endorsed by the high correlation between the forecasted and actual time-series forecasted.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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