Research on Deep Learning Based-carbon Measurement Model in UHVDC System
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it