Total electricity generation dynamics analysis and renewable energy impacts in South Africa
Bibliographic record
Abstract
Abstract This research explores the dynamics of total electricity generation (TEG) in South Africa through an analysis of data from the International Energy Agency database from 1990 to 2020. A comprehensive examination of various energy sources, including coal, oil, biofuels, nuclear, hydro, solar photovoltaic (PV), solar thermal, and wind, is conducted to ascertain their respective contributions to TEG. Employing the R software environment, the study employs a methodical analytical framework encompassing meticulous data preparation, statistical analysis, and model formulation. The data preparation phase involves intricate processes such as structuring, cleansing, and visualization aimed at eliminating stochastic variables and outliers. Missing data are addressed through the application of the Piecewise Cubic Hermite Interpolating Polynomial method. Subsequent statistical analyses are informed by tests for normality and homogeneity of variance, revealing deviations from normality and disparate variances across energy source groups. Consequently, non‐parametric methodologies such as the Kruskal–Wallis test are adopted. Findings underscore the significant role of nuclear energy in TEG despite facing challenges. Model development entails the construction of multiple linear regression models with varying predictor sizes, with Model m06 emerging as the optimal choice, incorporating key predictors such as coal, nuclear, and solar PV. Rigorous diagnostic assessments confirm the robustness of Model m06 and its suitability for TEG prediction. Comparative analysis against actual data validates its superior performance, characterized by minimal errors and high predictive accuracy. The efficacy of Model m06 in capturing TEG dynamics underscores its utility for informing energy planning initiatives. Recommendations derived from the study advocate for prioritizing renewable energy integration, infrastructure investment, research endeavors, monitoring mechanisms, and public awareness campaigns to advance sustainable energy development goals in South Africa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".