Prospects of Smart Metering System in Bangladesh: Cost Benefit Analysis
Bibliographic record
Abstract
One of the most significant parts of smart grid is Smart Metering System. They felicitate the information exchange between the service providers and the consumers, resulting in efficient management in the electricity supply. Many Western countries like Great Britain (GB) and Canada have conducted Cost Benefit Analysis (CBA) before starting installation of Smart Meter. The aim of this study is to compare the benefits from the Smart metering system over traditional (electro-mechanical and prepaid) meters, in accordance with Cost Benefit Analysis. So the main focus is to put emphasis on the Cost Benefit Analysis. The most important fact to analyse before the installation of Smart Meters is to find out the advantages of such technology as well as the cost behind the setup of such meter. Such as the degradation of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission, the installation and maintenance expenses, financial savings from the Time-of-Use (ToU) etc. The establishment cost of installing Smart Meter may be high but in general, the roll-out offers a 1.36 benefit-to-cost ratio (BCR), which reflects the costs and benefits of lifetime. In this study, Chittagong Municipality Area was chosen to find out how can the prepaid meters be replaced with the Smart Meters within an approximate minimal expense. From the very beginning of installing Smart Metering system, the required costs that have been calculated for installing Smart Metering system include operational cost, maintenance cost, meter cost, In-home display cost, Data Communications Company (DCC) cost etc. The estimated total cost of this project is set to be approximately ${\$}$ 300.9M.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".