Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-technical losses (NTL), which occur up to 40% of the total electric transmission and distribution power, create many challenges worldwide. These losses have a severe impact on distribution utilities and adversely affect the performance of electrical distribution networks. Furthermore, the depreciation of these NTL reduces the requirement of new power plants to fulfill the demand-supply gap. Hence, NTL is an emerging research area for electrical engineers. This paper proposed a model for the detection of non-technical losses based on machine learning and feature engineering. Experimental results check the performance of the proposed model. These results clearly show that this proposed detection model has better accuracy, precision, recall, F1 score, and AUC score than other existing approaches.
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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 | 0.000 | 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