Comparative Predictive Analysis through Machine Learning in Solar Cooking Technology
Bibliographic record
Abstract
Renewable energy technology has helped solve global environmental issues in recent years. Solar cooking technology is a sustainable alternative to conventional cooking, particularly in regions with ample sunlight. Although there is a growing interest into solar cooking, however, there is a lack of comprehensive comparison research upon the machine learning models predictive accuracy. Prior studies frequently concentrate upon individual models or fail to conduct comprehensive comparative analyses, resulting in a knowledge deficit regarding the most effective predictive methodologies for solar cooking technology. This research article compares solar cooking with special types of cooking utensils used for indoor cooking by predictive analysis of different kinds of machine learning models. To achieve proper cooking, the temperature of both pan and pot is to be monitored constantly. For this, a machine learning (ML) system model was constructed for predicting pan and pot temperature as a response parameter. By leveraging datasets encompassing time duration of the cooking, mass flow rate of heat transfer fluid, type of heat transfer fluid, and global solar radiations, a range of machine learning algorithms, including decision tree regressor, linear regression, extreme gradient boosting, and random forest regressor algorithms, are employed for predicting pan and pot temperature of solar cookers. Extreme gradient boosting is the best machine learning model for solar utensil temperature, with maximum R2 and minimum mean squared error, mean absolute error, and root mean squared error values that perfectly predict all answers. Also, extreme gradient boosting predicts well on training and testing datasets, whereas Random forest predicts well on training datasets but poorly on test data, causing overfitting. This research shows that machine learning could revolutionize solar cooking technology, promising a future for renewable energy and sustainable living.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".