Evolution of AI in Grid-Connected Renewable Energy Systems: A Systematic Literature Mapping
Bibliographic record
Abstract
The integration of artificial intelligence in grid-connected renewable energy systems is gaining increasing attention as a means to enhance efficiency, stability, and intelligent control.Yet, despite the growing interest, existing studies remain scattered across disciplines, lacking a structured overview of the research landscape.This paper presents a systematic literature mapping that investigates and synthesizes the current state of research through the lens of six key research questions.By applying a structured search strategy to the Scopus database, we identified 232 peer-reviewed articles published between 2014 and 2025 that met clearly defined inclusion and exclusion criteria.Each study was examined using both quantitative and qualitative analysis, covering key aspects such as dominant AI methods, renewable energy sources, main applications, simulation environments, collaboration networks, and the extent of real-world hardware implementations.Visualization tools like VOSviewer and Bibliometrix were used to map publication trends and co-authorship patterns.The findings reveal a sharp increase in research activity after 2020, with machine learning and neural networks leading the way, particularly in applications related to solar PV and hybrid systems.Most efforts are concentrated on simulation-based optimization and forecasting, while hardware integration is still underrepresented.This review not only maps out the current research landscape but also highlights research gaps and points toward promising directions for future interdisciplinary work and practical deployment.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".