Novel AI-Genetics Algorithm for Renewable Bioenergy Generation in City Centers
Bibliographic record
Abstract
Transforming cities into net producers of energy allows urban environments to mitigate the challenges of climate change. Such a transformation requires navigating efficient typologies of new ecosystems and buildings by integrating renewable energy sources into existing infrastructure. Bioenergy is among such sources that harness solar energy to produce biomass, act as natural carbon sinks, and generate oxygen through photosynthesis to improve air quality and support urban resilience. This study constructed a full-scale bioenergy system, with real-time biomass monitoring facilitated by an RGB image-based technique. Neural Networks were then integrated into Genetic Algorithms to improve search efficiency and reduce optimization time. Datasets containing fewer than 100 points exhibited lower predictive accuracy, with a 10-point Neural Network yielding a correlation coefficient of 0.661, and a 50-point Neural Network showing a correlation coefficient of 0.599. As the dataset size increased beyond 150 points, the Neural Network achieved a perfect linear correlation, reflected in a correlation coefficient of 1. Neural Network-Aided Genetic Algorithm optimized the system in just 6 minutes, a remarkable enhancement over the original algorithm that required 24.4 hours. Overall, the study highlights the potential of integrating AI-driven optimization and monitoring techniques into bioenergy systems, contributing to sustainable building designs that enhance renewable bioenergy production.
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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 | 0.000 | 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 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".