Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta
Bibliographic record
Abstract
Due to the depletion of fossil fuels and environmental concerns, renewable energy has become increasingly popular. Even so, the economic competitiveness and cost of energy in renewable systems remain a challenge. Optimization of renewable energy systems from an economic standpoint is important not only from the point of view of researchers but also industry owners, stakeholders, and governments. Solar collectors are one of the most optimized and developed renewable energy systems. However, due to the high degree of nonlinearity and many unknowns associated with these systems, optimizing them is an extremely time-consuming and expensive process. This study presents an economically optimal design platform for solar power plants with a fast response time using machine learning techniques. Compared with traditional mathematical optimization, the speed of economic optimization with the help of the machine learning method increased by up to 1100 times. A total of seven continuous variables and three discrete variables were selected for optimization of the parabolic trough solar collector. The objective functions were to optimize the exergy efficiency and the heat cost. As part of the environmental assessment, the cost of carbon dioxide emission was calculated based on the system's exergy and energy efficiencies. According to the sensitivity analysis, the mass flow of working fluid and the initial temperature of the fluid play the most significant roles. A simulated solar collector in Calgary was optimized in order to evaluate the applicability of the proposed platform.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".