Design of a smart meter techno-economic model for electric utilities in Ontario
Bibliographic record
Abstract
By the end of 2010, the Ontario Ministry of Energy and Infrastructure has mandated that every residential home in Ontario is to have a smart meter installed [12]. To complement this switch to smart meters, a techno-economic model comparing various functionality levels of smart meters has been designed. The model was created from the perspective of an Ontario local distribution company (the model is readily adaptable for use by utilities outside Ontario) to assist in determining the most viable feasibility level for the utility. Three main levels of functionality were used for this study: Minimum Functionality Smart Meters, Smart Meters with In-Home- Display, and Smart Meters with a Demand Control Unit. In the model, these functionality levels were compared based on the annual profit obtained and the overall reduction in energy consumption achieved. The annual profit was calculated by subtracting the installation, operating and maintenance costs from the annual revenue received from customers. The model itself does not provide an exact recommendation for the utility, but is intended to assist in the utility's decision making process. Based on case studies, it was observed that using smart meters with a minimum functionality level was most profitable. However, it was also observed that the greatest reduction in energy usage during peak demand periods occurred when demand control units were incorporated into the system. An appropriate strategy for a utility would be to invest in the functionality level that optimizes between the annual profit, the reductions in peak energy, and affordable capital costs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".