Artificial intelligence framework for smart city microgrids: State of the art, challenges, and opportunities
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
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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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