Study of smart grid for Thailand and identification of the required research and development
Bibliographic record
Abstract
Global warming necessitates a variety of responses including efficient energy use to reduce carbon emissions. The associated cost reduction should affect economic growth in general. For electricity, smart grid is an upcoming technology being applied currently in developed countries. Australia, Canada, China and the United States are planning to finish the smart grid in 2010–2012, while the European Union has been applying it since 2005. We believe Thailand should start considering this technology immediately. This paper deploy technology roadmapping approach to identify the research and development needed to support the smart grid in Thailand. We will define the smart grid and discuss its current status in Thailand. Its establishment will require the employment of a collection of technologies, including information, operational, communication, energy and consumer technologies. Ongoing projects and ready-to-use technologies will be reviewed, the policies and plans related to electricity delivery infrastructure will be analyzed, and Thailand's readiness for smart grid will be assessed. The main focus will be on identifying the existing problems that need further research and development. By analyzing capabilities of Thailand's research and by surveying the market, some predictions for next essential developments can be made. This paper will be useful for research organizations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".