Deep Learning-Based Prediction of Line Losses in Medium- and Low-Voltage Distribution Networks
Why this work is in the frame
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Bibliographic record
Abstract
A physics-informed deep learning framework is developed for line loss prediction in medium- and low-voltage distribution networks, directly embedding electrical governing equations and energy conservation into the model architecture. This strategy combines empirical data fitting with explicit physical regularization by using a hybrid loss function. As a result, the neural network is able to simultaneously identify statistical dependencies and domain-specific constraints. OpenDSS provides a rigorous environment for training and evaluation, simulating the generation of large-scale synthetic datasets under different topology conditions and loads. The framework is benchmarked against unconstrained deep models and traditional state estimation, and used for real-world validation using operational data from a provincial utility. The results show increased resilience to dynamic load and topology changes, improved prediction accuracy, and reduced consistency of outlier errors. The hybrid structure ensures computational efficiency for real-time deployment. The results show that incorporating physical knowledge into the neural architecture can improve the breadth and reliability of datadriven power system analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 it