How internet concepts and technologies can help green and smarten the electrical grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several powerful forces are gathering to make fundamental and irrevocable changes to the century-old grid. The next-generation grid, often called the 'smart grid,' will feature distributed energy production, vastly more storage, tens of millions of stochastic renewable-energy sources, and the use of communication technologies both to allow precise matching of supply to demand and to incentivize appropriate consumer behaviour. These changes will have the effect of reducing energy waste and reducing the carbon footprint of the grid, making it 'smarter' and 'greener.' In this position paper, we discuss how the concepts and techniques pioneered by the Internet, the fruit of four decades of research in this area, are directly applicable to the design of a smart, green grid. This is because both the Internet and the electrical grid are designed to meet fundamental needs, for information and for energy, respectively, by connecting geographically dispersed suppliers with geographically dispersed consumers. Keeping this and other similarities (and fundamental differences, as well) in mind, we propose several specific areas where Internet concepts and technologies can contribute to the development of a smart, green grid. We also describe some areas where the Internet operations can be improved based on the experience gained in the electrical grid. We hope that our work will initiate a dialogue between the Internet and the smart grid communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| 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 it