This study presents the design and performance evaluation of a solar cooker utilizing a reused evacuated tube as its primary cooking element. The cooker’s design prioritizes enhancing thermal efficiency through the incorporation of a parabolic reflective surface and high-conductivity materials, aiming to reduce environmental impact by repurposing discarded components. Experimental tests demonstrated that the prototype outperformed alternative designs, including a parabolic dish and a replica of the commercial Haines® parabolic cooker. This represents an approximate 65% improvement in thermal performance, making the proposed design suitable even under cloudy weather conditions. Moreover, the prototype successfully baked a wheat-flour cookie, indicating its viability for practical food preparation. The findings suggest that such solar cookers could serve as an eco-friendly and cost-effective alternative in areas where fossil fuels are expensive or scarce. Limitations, including non-uniform temperature distribution inside the evacuated tube and the durability of reused materials, are discussed. Recommendations for future research include integrating phase-change materials to enhance heat retention and expanding comparative studies to other types of solar cookers.

The reliable operation of photovoltaic (PV) systems requires effective methods for distinguishing anomalies from normal environmental variations. This study presents an Artificial Intelligence of Things (AIoT) framework for real-time PV monitoring and anomaly diagnosis. The proposed system integrates custom data acquisition, MQTT communication, edge computing, an LSTM Autoencoder, and Random Forest classification. Experimental validation was conducted under real operating conditions at three scales: a 500 W microinverter-based system, a 3.9 kWp grid-connected installation, and a 62.5 kWp PV subsystem. The evaluated conditions included normal operation, environmental variations, inverter clipping, shading, dust accumulation, and localized hotspot conditions. The Random Forest classifier achieved 99.02% test accuracy for the classification of seven operating conditions, while the hybrid strategy subsequently identified localized and progressive anomalies through inter-string comparison and persistence-based rules. The results demonstrate the feasibility of low-latency PV anomaly diagnosis using edge intelligence with reduced dependence on continuous cloud processing.