Performance Prediction Model Development for Solar Box Cooker Using Computational and Machine Learning Techniques

Abstract

Abstract The development of prediction models for solar thermal systems has been a research interest for many years. The present study focuses on developing a prediction model for solar box cookers (SBCs) through computational and machine learning (ML) approaches. The prime objective is to forecast cooking load temperatures of SBC through ML techniques such as random forest (RF), k-nearest neighbor (k-NN), linear regression (LR), and decision tree (DT). ML is a commonly used form of artificial intelligence, and it continues to be popular and attractive as it finds new applications every day. A numerical model based on thermal balance is used to generate the dataset for the ML algorithm considering different locations across the world. Experiments on the SBC in Indian weather conditions are conducted from January through March 2022 to validate the numerical model. The temperatures for different components obtained through numerical modeling agree with experimental values with less than 7% maximum error. Although all the developed models can predict the temperature of cooking load, the RF model outperformed the other models. The root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and mean square error (MSE) for the RF model are 2.14 (°C), 0.992, 1.45 (°C), and 4.58 (°C), respectively. The regression coefficients indicate that the RF model can accurately predict the thermal parameters of SBCs with great precision. This study will inspire researchers to explore the possibilities of ML prediction models for solar thermal conversion applications.