Design and Development of an Intelligent Tool for Retail Electric Provider Plan Selection
Bibliographic record
Abstract
In this paper, an intelligent tool for retail electric provider plan selection is designed and developed for the residential customers and prosumers. The main objective of this tool is to provide decision support to the residential customers to enable them to make a feasible selection of local distributed energy resources such as rooftop solar photovoltaic and battery energy storage as well as plug-in electric vehicles that will maximize the savings on their energy bill. In conjunction with decision support in finding feasible combinations of resources, a suitable retail electric provider is also chosen that will minimize their annual energy bill. In total, 48 homes were evaluated, 17 representative profiles of rooftop solar photovoltaic, home battery energy storage and plug-in electric vehicles as well as 24 retail electric provider plans consisting of flat, tiered and time-of-use plans were evaluated from Austin, Texas. This tool was designed using a knowledge base of rules for decision support and the difference in the minimum annual energy bills, before and after the resources are added to the homes, are used to calculate the personalized energy bill savings of the prosumers. This tool was implemented in MATLAB App-designer software.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".