Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation
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
Abstract To achieve low‐carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero‐carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO 2 emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO 2 emission.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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