| dc.description.abstract | Abstract. Against the backdrop of the in-depth advancement of the green transformation and sustainable development of the global energy structure, renewable energy, especially solar, and geothermal energy technologies, with their clean and renewable characteristics, play an irreplaceable and crucial role in achieving carbon reduction targets and ensuring energy security and autonomy. In photovoltaic (PV) systems, elevated panel temperatures significantly impair power conversion efficiency. To mitigate this issue, the present study develops a comprehensive simulation model for a novel energy-efficient residential building that integrates a photovoltaic/thermal (PV/T) system with an earth-to-air heat exchanger (EAHE). The model adopts instantaneous electrical efficiency and the adaptive predicted mean vote (APMV)—representing, respectively, the energy performance of the PV/T subsystem and the indoor thermal comfort—as dual optimization objectives. Key structural parameters influencing system performance are systematically identified, and a multiobjective optimization is conducted using the Nondominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal design configuration that achieves a balanced enhancement in both energy efficiency and thermal comfort. The optimized design of the system resulted in a maximum increase of 6.3% in average PV panel power efficiency and a maximum increase of 3.31% in average APMV. Multiple optimization objectives are aggregated into a single objective, namely the cumulative net energy consumption. A genetic algorithm is then employed for optimization design to determine the value of the structural variable at which the cumulative net energy consumption is minimized. The cumulative net energy consumption of the optimized system is 2605.94 kWh, which is reduced by 70.69% compared with the original system, and the energy-saving effect is remarkable. | |
