Renewable Energy and Artificial Intelligence: Smart Energy Management Models for Developing Cities
Bibliographic record
Abstract
Against the background of intensive urbanization, the growing cities are faced with considerable difficulties in the area of sustainability, but they are also promising fields of innovative development.This paper questions how artificial intelligence is used in renewable energy systems, focusing on the design of such systems, their implementation, and the empirical evaluation of smart energy management models specific to the kind of needs of the emergent urban environment in less developed countries.Our study, based on a series of AIbased experiments, includes structured surveys, field surveys, and advanced statistical reviews, proving that it is possible to achieve up to fifteen percent energy efficiency, up to twelve percent grid reliability, and user satisfaction, which is more than an order of magnitude higher when compared to traditional methodologies.This empirical assessment is a detailed overview of the latest literature that is indexed in Scopus in the period 2022-2025, which shows the current development and points out the significant gaps, especially in the social and organizational aspects.The systems theory, optimization theory, and principles of responsible artificial intelligence are the methodological basis of the approach, where the inquiry is both technically sound and ethically sound.Its results highlight the potential changes in artificial intelligence that can be applied in the provision of scalable and reliable management of renewable energy sources in resource-constrained urban settings.The conclusion of the paper is to suggest ways of integrating the policy, to make AI human-oriented, and to provide future research directions that will support sustainable urban development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".