An Applied Model for Identification and Evaluation of Factors Affecting Energy Losses of Electric Distribution Network Case Study: Selected Counties of Bushehr Province
Bibliographic record
Abstract
From its generation to utilization, some of the electrical energy gets wasted in the process. This loss of energy occurs due to various reasons, one of which is energy loss in distribution networks. Considering the high cost of power generation, it is important to identify factors causing this loss. This study was carried out with the objective of identifying energy loss factors and the importance of each factor. Lack of identification for factors stealing energy, network deterioration, amount of electrical load and the impact of such factors that can have significant influence on energy loss could diverge the path of energy management. Thus, the main objective of this study was to reduce energy loss and its additional costs by developing the concept of identifying influential factors and measuring the effect of each factor especially in different regions. The statistical population of this study comprised of power and energy experts and university professors. The statistical sample included 12 energy experts and their opinions were collected using questionnaires and paired comparisons. Weights of criteria were determined using SWARA technique. COPRAS-G technique was used for measuring the importance of criteria for Bushehr province distribution networks. The importance of criteria are: energy theft, measurement error, amount of load, network deterioration, loose fittings, improper placement of equipment, the amount of voltage, conductor resistance, equipment casualty, location and size of the capacitor, geographical conditions, Size and dimensions of the conductor, leakage, and network arrangements respectively. Distribution network of Assaluyeh region had the highest energy losses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".