Comparative Predictive Analysis through Machine Learning in Solar Cooking Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| 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 it