BC Hydro's methodology for energy losses assessment in distribution systems
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
Current utility business and regulatory environments impose new demands and constraints on the development, operation, and maintenance of distribution systems. There is increasing pressure to ensure that all aspects of distribution utility operations are appropriately and optimally managed. The strong interest in achieving efficiency targets related to energy conservation is encouraging utilities to incorporate and implement more rigorous investments plans for distribution losses reduction. For this reason, utilities need to understand the cause of the energy losses, the methodologies to assess them, and the measures to reduce them in a cost-effective manner. This paper presents a methodology for assessing technical and non-technical energy losses in distribution systems that has been successfully implemented at BC Hydro. It also discusses techniques to manage and reduce energy losses.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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