Sensitivity Analysis of Exogenous Variables for Load Forecasting Using Polynomial Regression
Bibliographic record
Abstract
The choice of explicative variables could influence the efficiency of power consumption prediction. Different techniques based on a sensitivity analysis permit to choose the best set of variables among all candidates. Therefore, studies introduce a different set of factors like the temperature to construct a useful prediction system. The goal of this paper is to define the best variable candidate that can efficiently describe power consumption. Accordingly, a model is developed to integrate both meteorological and complementary variables for load forecasting. We use, therefore, powerful tools based on, namely ANOVA, ANCOVA, and Backward Elimination to identify the significant factors that will be inserted in the Principal Component Analysis (PCA) for household and aggregated levels. Mainly, the objective of PCA is to minimize the dimension of the data-set and maximize the information entropy over distinct un-correlated principal extractions. Consequently, the application of polynomial regression to principal components for load forecasting conducts to better results providing a useful forecast by capturing the best explanatory variables.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".