Performance Evaluation of Control Methods for PV-Integrated Shading Devices

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.

Daylighting Performance and Thermal Comfort Performance Analysis of West-Facing External Shading for School Office Buildings in Cold and Severe Cold Regions of China

“…Standard parameters studied include the width of the louvers [22], the optimal inclination of the louvers [23], and the reflectance and transmittance of the louver material [24]. In terms of evaluation indexes, the energy consumption index, and energy saving rate; in terms of the thermal comfort index: PMV [25,26], temperature, and cumulative solar radiation [25,27]; and in terms of the light comfort indexes: daylight glare index (DGI) [28,29], daylight glare probability (DGP) [30,31], useful daylight illuminance (UDI) [32,33], and daylight factor (DF) [34][35][36] are often used in the study. In addition, several studies propose optimal control schemes for louver shading in different climate zones, orientations, and periods [31,37,38].…”

A systematic review on predicting PV system parameters using machine learning

“…It is more prominent in solar experiments since researchers can postulate solar panels' performance and durability using computational tools in either simulation or practical experiments. During our screening, we found that some authors performed their studies practically in real time [35] , [36] , [58] , and many authors adopted simulation tools with curated data sets to obtain their findings [5] , [67] , [4] . In another situation, simulation and practical approaches have been applied in the same experiments for a better comparative analysis [50] , [61] , [41] .…”

“…Table 3 reveals that ANN wins in terms of best performing ML methods [38] , [32] , [50] , [54] , [58] , [61] . Jung et al [54] achieved 97% score in their testing dataset using ANN, whereas Kim and Kim [36] achieved 96.42% and Zhao et al [50] achieved 82.90% score.…”

Paper Review of External Integrated Systems as Photovoltaic Shading Devices

“…In Korea, at a latitude very close to that of Athens, it has been noticed that simple geometry shading systems do not differ in efficiency compared with standard photovoltaic roof solutions [18]. The research in [68] proposes a BIPV device and optimal control method that increases the PV efficiency, maintaining visual comfort. The analysis was performed on a small-scale prototype of a PV louvre.…”