Deep Learning-Based Prediction of Line Losses in Medium- and Low-Voltage Distribution Networks
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".