Predicting the Performance of Solar Cooker using Machine Learning Algorithms

We have designed this machine learning model to predict the performance of different solar cookers in various operating conditions. Basically, our model predicts the maximum temperature that can be achieved by a solar cooker based on factors like cooker-type, vessel-type and experimental conditions. In our model we have used three different regression algorithms such as Random Forest algorithm, Support Vector Regression (SVR) and Linear Regression. These three models have been used for making predictions and their results are compared. We have collected the data about different solar cookers by experimenting under various operating conditions. We took this data and trained our model using various Regression algorithms. After training, the model is tested and the performance of the model is calculated using evaluation metrics like Mean Squared Error (MSE) and R^2 (R-Square) score. Among these three machine learning models, Linear Regression algorithm made more accurate predictions. Our study emphasizes the potential of Machine Learning in solar cooking technologyleading to sustainable development.