The Impact of Reduced Non-technical Distribution Losses on GHG Emissions by Implementing Advanced Metering Infrastructure
Bibliographic record
Abstract
The distribution losses in the electrical system demonstrate the reliability and efficacy of the network in providing electricity to the customers. The PLN Statistical Report 2022 stated that 20,236 GWh of electricity became losses at PLN’s distribution network across Indonesia. Not only causing the financial disadvantage, but these losses are also believed to have an adverse effect on the environment since they raise the amount of greenhouse gas (GHG) emissions because energy losses need to be compensated. Thus, various initiatives are conducted to improve the performance of distribution network system, including in Bali. In attempt to minimize losses, PLN Bali Distribution Unit has gradually implemented Advanced Metering Infrastructure (AMI) technology throughout 2023. This research is supposed to examine the impact of the AMI installation on the value of losses and GHG emissions. Our findings suggest that AMI technology has a positive impact on non-technical losses but an insignificant impact on total losses, defying the widely held belief that it can notably reduce losses. The environmental impact then can be quantified by converting the losses value to GHG emissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".