Research on Deep Learning Based-carbon Measurement Model in UHVDC System
Bibliographic record
Abstract
With the increasing application of the Ultra-High Voltage Direct Current (UHVDC) system, carbon emissions produced by these systems are growing in proportion within the power industry. However, several challenges exist in reducing carbon emissions for the UHVDC system, including difficulties in processing high-dimensional and strongly coupled data and establishing electrical-to-carbon conversion models. Considering this, this paper investigates the carbon measurement issue in the UHVDC system as follows: First, the loss mechanism is analyzed to determine its distribution. Then, considering the characteristics of loss, a factor accounting algorithm is selected to calculate the electrical-to-carbon conversion for the system. Finally, a carbon measurement model is constructed, combining deep learning models. To address the challenges of feature extraction and fusion in the UHVDC system, a model fusion strategy based on Residual Network and Long Short-Term Memory (LSTM) is proposed. Experimental results demonstrate that the evaluation indexes of the fusion carbon measurement model are superior to other deep learning models. Compared to the LSTM, the fusion algorithm model reduces mean squared error, root mean squared error, mean absolute error, and mean absolute percentage error by 30.51%, 20%, 16.67%, and 2.17% respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".