Optimized Feature Selection Based on a Least-Redundant and Highest-Relevant Framework for a Solar Irradiance Forecasting Model
Bibliographic record
Abstract
Exogenous and endogenous variables are typically evaluated several times during the selection trial of a predictive model for Global Horizontal Irradiance (GHI). This is accomplished using various statistical measures (e.g., univariate statistical analysis, correlation analysis, etc.) that are applied to gauge redundancy and relevancy in specific variables. The main benefits of these approaches include lower computational cost, fast screening times, accurate measuring of linear and monotonic degrees of variable pairs, and the removal of features with low relevance. However, they cannot identify instances where single or groups of predictor variables are non-monotonically associated with the response variable, nor can they discern whether variables are predictive in combination with other variables or in isolation. The present study attempts to overcome these challenges by first describing monotonic and non-monotonic (Spearman’s rho and Hoeffding’s D, respectively) correlation statistics in combined usage for locating groups with major non-monotonic endogenous variable changes. The proposed work’s novelty is subset evaluation that determines relevance using Weather Recursive Feature Elimination (WRFE). This is a novel hybrid feature reduction method that optimizes feature selection using a Least-Redundant/Highest-Relevant framework. The proposed WRFE utilizes feature importance for measuring variance reduction in Random Forest Regression (RFR) and as data perturbation in Long Short-Term Memory (LSTM). The simulation results of GHI hourly predictions demonstrate that the proposed optimal features of the training subset make the greatest contributions to the prediction target, proving that the high variability of irradiance conditions lowers training subset reliability.The results showed that the proposed WRFE is superior compared to the other models with 1.0927 % for the RMSE and the$R^{2}$coefficient is exceeding 98%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".