Artificial intelligence framework for smart city microgrids: State of the art, challenges, and opportunities
Bibliographic record
Abstract
Smart city concepts have gained substantial attention over the last few years, as they apply advances in Information and Communication Technology (ICT) to enhance the quality and efficiency of services and resources. Microgrids are potentially powerful building blocks in the development of smart cities. Motivated by the opportunity, this article examines the factors leadings to the adoption of microgrids for mainstream electrical utilities grids, discusses the benefits that drive the growth, identifies the issues hindering benefit-capture of distributed energy generation inside microgrids, and provides a framework for the application of Artificial Intelligence (AI) to overcome challenges. We examine a simulation framework scenario and useful data sources that can help build AI capabilities within utilities. A brief description of the scalable BluWave-ai framework that leverages deep learning in the data centre is also provided, and AI inference at edge computing nodes and IoT sensors to optimize the benefits from microgrids at residential, neighbourhood, campus, enterprise and community levels is examined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".