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View Factor를 고려한 마이크로그리드 적용용 고효율 P-Type Si 양면형 태양광 모듈의 출력량 예측
최진호,김광순,차혜림,김규광,방병관,박소영,안형근,Choi, Jin Ho,Kim, David Kwangsoon,Cha, Hae Lim,Kim, Gyu Gwang,Bhang, Byeong Gwan,Park, So Young,Ahn, Hyung Keun 한국전기전자재료학회 2018 전기전자재료학회논문지 Vol.31 No.3
In this study, 20.8% of a p-type Si bifacial solar cell was used to develop a photovoltaic (PV) module to obtain the maximum power under a limited installation area. The transparent back sheet material was replaced during fabrication with a white one, which is opaque in commercial products. This is very beneficial for the generation of more electricity, owing to the additional power generation via absorption of light from the rear side. A new model is suggested herein to predict the power of the bifacial PV module by considering the backside reflections from the roof and/or environment. This model considers not only the frontside reflection, but also the nonuniformity of the backside light sources. Theoretical predictions were compared to experimental data to prove the validity of this model, the error range for which ranged from 0.32% to 8.49%. Especially, under $700W/m^2$, the error rate was as low as 2.25%. This work could provide theoretical and experimental bases for application to a distributed and microgrid network.
신연배(Shin Yeon-Bae),김규광(Kim Gyu-Gwang),조용현(Cho Yong-Hyun),조영호(Cho Young-Ho),양태열(Yang Tae-Youl) 한국태양에너지학회 2023 한국태양에너지학회 논문집 Vol.43 No.6
Adding various colors to photovoltaic (PV) modules is essential for the building-integrated photovoltaics (BIPV) market; however, it can also reduce PV performance and increase manufacturing costs. Therefore, a tool is required to predict PV performance based on color before production of PV modules. In this study, we demonstrate an approach for predicting the performance of colored PV modules before they are produced. First, we analyzed the optical properties of various colors of polyvinyl chloride (PVC) films available in the market and their correlation with power. A total of 15 prediction models were trained. Then, we manufactured colored films for BIPV, predicted their power using the trained models, and compared the predictions with actual power values. Additionally, we validated and optimized the prediction models using mean absolute error (MAE) and root mean squared error (RMSE). We achieved a low MAE of 3.8% and an RMSE of 4.3% using a prediction model formula that consisted of transmittance or a combination of transmittance and absorption. This approach offers several advantages, such as the ability to predict the performance based solely on the optical properties of front-colored films or glass used in colored BIPV without the need for sample production, which can save time and resources.