A New Selection Tool of Retail Electric Provider for Prosumers With Local Distributed Resources and Plug-in Electric Vehicles
Bibliographic record
Abstract
This study addresses the issue of identifying the most suitable retail electric provider plan for the residential customers that are consumers only as well as consumers who intend to become prosumers. The main objective is to develop a methodology that will assist the residential customers in identifying the most suitable distributed energy resources, such as rooftop solar photovoltaic and home battery storage as well as electric vehicles. Furthermore, the proposed methodology is used to develop a decision support tool that aims to determine the appropriate retail electric provider plan that will ensure maximum savings on the customer's energy bill. The results of implementing the proposed methodology on case studies from Texas have shown that the residential customers can achieve significant savings if they install rooftop solar photovoltaic. The results have also shown that the maximum savings are usually associated with the time-variant or time-invariant plans, in case of customers with low energy usage, reaching up to $221/year. On the other hand, the customers with high energy usage were found to achieve significant savings on their energy bills when they are on the tiered time-invariant plans, in which case the savings may reach up to $475/year.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".