Enhancing Efficiency of Photovoltaic Thermal Systems in Tropical Climates: A Review of Automated Control Strategies, Cooling Mechanisms, and Experimental Insights
Bibliographic record
Abstract
This review consolidates research on enhancing the performance of Photovoltaic Thermal (PV/T) systems through automated systems, control, and optimization, specifically addressing efficiency losses attributed to high irradiance and ambient temperatures in tropical regions.The review's objectives include evaluating automated control and optimization techniques, benchmarking cooling methods, analyzing experimental validations and case studies, comparing industrial applications, and identifying emerging trends in PV/T technologies.A systematic analysis was performed on empirical, numerical, and hybrid studies from tropical and subtropical climates, with a focus on control strategies, cooling performance, experimental rigor, deployment scale, and the adoption of innovations.The key findings suggest that efficiency gains of up to 28% can be achieved through advanced automated controls and hybrid cooling systems.Effective thermal management methods include water cooling, nanofluids, and phase change materials (PCM).The review also emphasizes robust experimental validations conducted under real tropical conditions, though it notes that industrial-scale applications are limited by economic and scalability challenges.Furthermore, the integration of AI-driven optimization and smart control systems shows promising potential but requires additional field validation.Collectively, these findings highlight critical gaps in long-term stability, cost-effectiveness, and adaptive control under diverse tropical climates.The review underscores the necessity for scalable and economically viable PV/T solutions that incorporate advanced materials and intelligent systems to optimize performance and facilitate sustainable energy adoption in tropical environments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".