AI-Based Maximum Energy Harvesting and Power Management of a Hybrid PV–Geothermal-Emulated System Using Thermoelectric Generation in a Closed-Loop Water Channel
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Abstract
In this study, an artificial intelligence (AI)-based maximum energy harvesting and power management strategy for a hybrid photovoltaic–thermoelectric (PV–TEG) system with geothermal heat emulation using a closed-loop water channel is proposed. The system allows for a photovoltaic panel integrated into a thermoelectric generator which recovers thermal energy and ultimately increases total electrical output. Experimental and numerical studies reveal that the independent PV module powers at 100 W to 140 W while the thermoelectric generator adds 20–30 W at a maintained temperature difference of 16–20 °C. The hybrid system generates total power from 120 W to 160 W without AI control, and 125 W to 170 W with an AI-based power management output to create a peak power of approximately 168–170 W. AI-based voltage control has a positive influence on voltage stability, leading to a reduction from ±1.5 V (22.5–25.5 V) at conventional control to ±0.4 V (23.6–24.4 V). The total harvested energy increases from about 138 Wh to 145 Wh, approximately 5–6%, while the cumulative accumulated energy after 60 minutes increases to nearly 130 Wh. With less oscillation, the overall system efficiency rises from 10.5–16.2 percent to 11.3–16.9 percent. Moreover, at high irradiance levels, the AI-based maximum power point tracking (MPPT) method outperforming the conventional Perturb and Observe and Incremental Conductance techniques achieves a maximum PV harvested power of approximately 195 W which is higher than 180 W and 188 W. The most reliable classifiers outperform: Random Forest and ANFIS achieve classification accuracies of 96% and 95.2% with less than 5% errors, as well as being robust to measurement noise.
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