Performance Evaluation of Control Methods for PV-Integrated Shading Devices

Sung Kwon Jung, Youngchul Kim and Jin Woo Moon
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Sung Kwon Jung: CS Enertech, Seoul 06730, Korea
Youngchul Kim: Department of Civil and Environmental Engineering, KAIST Smart City Research Center, KAIST Urban Design Lab, KAIST, Daejeon 34141, Korea
Jin Woo Moon: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea

Energies, 2020, vol. 13, issue 12, 1-21

Abstract: This study aimed to develop a building-integrated photovoltaic (BIPV) device and optimal control methods that increase the photovoltaic (PV) efficiency and visual comfort of the indoor space. A louver-type PV-integrated shading device was suggested and an artificial neural networks (ANN) model was developed to predict PV electricity output, work plane illuminance, and daylight glare index (DGI). The slat tilt angle of the shading device was controlled to maximize PV electricity output based on three different strategies: one without visual comfort constraints, and the other two with visual comfort constraints: work plane illuminance and DGI. Optimal tilt angle was calculated using predictions of the ANN. Experiments were conducted to verify the system modeling and to evaluate the performance of the shading device. Experiment results revealed that the ANN model successfully predicted the PV output, work plane illuminance, and DGI. The PV-integrated shading device was more efficient in producing electricity than the conventional wall-mount PV systems, the control method without visual comfort constraints was most efficient in generating electricity than the other two with such constraints, and excluding the constraints resulted in less comfortable visual environment and reduced energy benefit. From the results analysis, it can be concluded that based on the accurate predictions, the PV-integrated shading device controlled using the proposed methods produced more electricity compared to the wall-mount counterpart.

Keywords: PV-integrated shading device; optimum louver slat angle; artificial neural networks; electricity production; visual comfort (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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