| Summary: | This study aims to develop and evaluate the performance of an asymmetric compound parabolic concentrator (ACPC) PV/T designed for building façade configuration, addressing the limitations of conventional symmetric compound parabolic concentrators (CPC) and flat type, double-pass photovoltaic thermal (PV/T) air solar collectors. The ACPC enhances solar incidence angle within the full acceptance angle for optimal performance in a façade configuration. However, accurately predicting the performance of the solar collectors remains a challenge due to the variations in ambient parameters. To overcome this, an Artificial Neural Network (ANN) model was developed and validated to predict system performance based on input ambient variables, such as solar radiation and temperature, with outputs representing the electrical and thermal performance of the PV/T collector. The validated ANN model was then used to conduct a simulation case study for the tropical climate of Malaysia. The simulation results demonstrated that the ACPC PV/T collector outperformed both symmetric CPC and flat-type PV/T collectors regarding electrical and thermal performance. Additionally, the ACPC PV/T had a shorter payback of 5.4 years, approximately 1 year shorter than the CPC and 2 years shorter than the flat-type PV/T. These findings suggest that the ACPC PV/T system offers a more efficient and cost-effective solution for façade-integrated systems. This study contributes to the body of knowledge on photovoltaic/thermal systems with three key contributions: first, an investigation of the underexplored asymmetric CPC for building façades; second, the utilization of a data-driven ANN model as a predictive tool, demonstrated through a simulation case study in combination with TRNSYS; and third, a comparative analysis of asymmetric CPC, symmetric CPC, and flat PV/T air solar collectors specifically for façade applications. This study, therefore, provides a comprehensive platform for further research on CPC PV/T systems for building façades. © 2024
|
|---|