Mock-Up 데이터 학습을 통한 반투광형 태양광(STPV) 모듈의 표면 온도 예측 모델 개발 = Development of a Surface Temperature Prediction Model for STPV through Mock-Up Data Learning

다국어 초록 (Multilingual Abstract)

Semi-Transparent Photovoltaics (STPV) panels are integrated into building windows to allow natural light transmission while generating electricity. However, conventional energy simulation tools often underestimate the indoor heat transfer, as they fail to accurately account for the thermal effects of STPV power generation. This study develops an indoor surface temperature prediction model for STPV modules to simulate the thermal behavior during power generation and enhance the reliability of energy performance assessments. Using measured mock-up data, Pearson correlation and multiple regression analyses were conducted, and power production per unit area (PPA), outdoor temperature (OT), and relative humidity (RH) were identified as the primary factors influencing the surface temperature of the STPV module. The heat transfer calculation involves comparing the surface temperature of the STPV module with the indoor temperature, and the model is applied only when the power production per unit area exceeds 2.32 Wh/㎡ for crystalline silicon modules and 4.13 Wh/㎡ for amorphous silicon modules. Overall, this approach provides a refined assessment of the indoor thermal performance of STPV systems, ultimately improving the reliability of energy performance evaluations.

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