Optimization of Performance Analysis of IoT-Based Temperature Monitoring Box Type Solar Cooker

The purpose of this book chapter is to investigate how a (NSGA-II) multi-objective genetic algorithm might be utilized to optimize the execution of an Internet of Things (IoT) temperature monitoring Box-Type Solar Cooker (BTSC). To determine the best set of output parameters for an IoT temperature monitoring box-type solar cooker, (NSGA-II) multi-objective genetic algorithm are used to perform optimizations of the Figureure of merits (F2), cooking power, cooker efficiency, and final water temperature. The present research work involved the development of a Wi-Fi module system integrated with a smart temperature monitoring system for a BTSC. We compare the values of the response variables that were gathered experimentally with the values that were predicted by NSGA-II. The predicted values are found to be quite close to the experimental values. This indicates that the multi-objective optimization method, as used in this study, has very good prediction performance. According to the findings of the experiment, the temperature at which a cooking pot remained stagnant on average was 158°C. It was determined that the cooker was of class A based on the values of the first Figureure of merit (F1), the second Figureure of merit (F2), and the cooking power (P), which were respectively 0.132, 0.359, and 86.108 W. Therefore, the thermal efficiency of the IoT-base temperature monitoring box type solar cooker is 39.99%. Optimize the performance of IoT-based BTSC by providing real-time monitoring and data visualization, ultimately improving their efficiency and reliability. This research provides an educational tool to promote awareness and understanding of renewable energy sources and their potential benefits.