A comprehensive review over advancements in solar cooking: Enhancing efficiency with AI and thermal energy storage

Solar cookers have evolved into an eco-friendly alternative to conventional cooking methods by reducing dependency on fossil fuels. Because of advancements in materials, insulation techniques, and reflector designs, their efficiency has significantly increased over time. Modern solar cookers incorporate PCM to enhance heat retention and enable cooking during non-sunny hours. PCM technology aids in the storage of thermal energy and improves overall cooking efficiency by absorbing excess heat and releasing it gradually. Recent studies have looked into using machine learning (ML) algorithms to optimize solar cooker performance. Regression models such as ANN, SVR, decision trees, and hybrid ML models have been used to predict temperature changes, optimize design parameters, and improve heat retention. Phase Change Materials (PCM) and Machine Learning (ML) have been combined to greatly increase the solar cookers' efficiency and versatility. ML-based predictive models will be essential in improving heat retention, optimizing reflector angles, and increasing overall thermal efficiency as AI-driven optimization advances further. This will make solar cooking more dependable and accessible for a range of climatic conditions. The present paper studies the advancement in solar cookers, experimental studies, and the best ML models used for predicting temperature, material performance, and cooker efficiency. Below figure shows the data acquisition of both PCM and non PCM for machine learning regression • Evolution of Solar Cookers – Traces the advancements in solar cookers, emphasizing eco-friendly alternatives to fossil-fuel-based cooking. • Improved Efficiency – Discusses innovations in materials, insulation techniques, and reflector designs that have enhanced solar cooking performance. • Phase Change Materials (PCM) for Heat Retention – Explores how PCM helps store thermal energy, enabling cooking even during non-sunny hours. • Machine Learning (ML) in Solar Cooking – Reviews the role of ML models in optimizing solar cookers by predicting temperature variations and improving efficiency. • Use of Regression Models – Highlights applications of ANN, SVR, decision trees, and hybrid ML models in predicting heat retention and optimizing design. • AI-Driven Optimization – Explains how ML techniques help refine reflector angles, material selection, and thermal performance for various climates. • Integration of IoT – Discusses the role of IoT in monitoring and controlling solar cookers for better efficiency and real-time optimization. • Experimental Studies – Summarizes experimental research on PCM-enhanced solar cookers and ML-driven performance enhancements. • Future Trends – Identifies potential advancements in AI, thermal storage, and automation to improve the reliability of solar cooking. • Impact on Sustainability – Emphasizes the contribution of AI and PCM in making solar cookers a viable, eco-friendly solution for global energy needs.