State of the art in energy consumption using deep learning models
Bibliographic record
Abstract
In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country’s higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".