Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression
Bibliographic record
Abstract
Abstract The G7 countries, consisting of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States, have important collaborations in energy production to achieve energy security. One of the main systems of Artificial intelligence's, artificial neural networks (ANN), is crucial to this area of study, comparatively using Multiple Linear Regression (MLR) comparatively. ANN and MLR are feasible to use across the G7 countries' total energy production numbers, and these numbers were determined using ANN and MLR forecasting techniques. The data included the years 1990–2020, focusing on GDP intensity, refined oil product production, electricity production, and renewable energy proportion. In ANN modeling, the best regression results at 10*10 have been obtained with two hidden layers. All regression values were 0.99947, with the training regression value being 0.99912, the validation regression value being 0.99997, and the test regression value being 0.99997. The results showed high accuracy, with regression scores exceeding 99% and smaller prediction error values. A paired sample t test has been applied to see whether the distinction between the average values is significant or not. The results of the test between the actual and predicted values (p = 0.7462 > 0.05) revealed that the forecasted values have been quite close to the actual values. Total energy production Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) parameters have been calculated as 4.364, 34.072, 5.837, and 0.162, respectively. The research proved that ANNs are effective in forecasting total energy output. And, with MLR, error values for MAD, MSE, RMSE, and MAPE were also found to be 5.364, 34.352, 5.861, and 1.609, respectively, using MLR modeling. By 2035, the G7 nations are expected to produce 50,652.746 Mtoe of energy collectively. The research proved that ANNs are effective in forecasting total energy output.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".