GEOSPATIAL, FINANCIAL, HUMAN, AND TEMPORAL FACTORS IN THE STUDY OF THE DEVELOPMENT OF RENEWABLE ENERGY AND SMART GRIDS
Bibliographic record
Abstract
Balanced development of smart grids is becoming an increasingly important issue for the energy sector's successful operation. This article provides a bibliographic review of publications in the study of renewable energy and smart grids' deployment parameters. A sample of works for 2009-2020 from the Scopus® database, which contains bibliographic information about scientific publications in peer-reviewed journals, books, and conferences, was selected for analysis. The authors identified three clusters of research areas using VOSviewer (version 1.6.15) in the context of the impact of geospatial parameters on smart grids' development. The first cluster consists of the financial, human, and temporal components of the geospatial factor of smart grid deployment. The authors found the largest number of links in the first cluster in terms of "costs" (a total of 29 links with an average impact of 9). The second cluster coincides with concepts related to geospatial information systems (GIS), digital storage, information systems, and cartographic information use. Research on renewable energy also belongs to the second cluster of publications. And the third cluster highlights all the concepts of smart grids by their technical types and in the context of optimization. The third cluster focuses on the ideas with the strongest link power. The results of the analysis of the Scopus® database allowed to determine the level and dynamics of scientific interest in the geospatial factors of the development of smart grids over the past 10 years. It is established that research in the field of geospatial factors of smart grid development is carried out by different countries, but the most active analysis of the impact of geospatial parameters on the development of smart grids in the following countries: USA, Canada and China. Based on the use of the Scopus® database, the article identified institutions and organizations that fund the study of geospatial factors and smart grids and made a significant contribution to the development of this topic.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.070 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".