Energy Catalyst

3103-9952

Caravel Press

10.61552/EC.2025.011

2025.011

Two Novel Models for Predicting the Transmittance of Dielectric Crossed Compound Parabolic Concentrator (dCCPC) Under Various Sky Conditions

https://orcid.org/0000-0001-7273-7683

Tian

Meng

https://orcid.org/0000-0002-1045-2022

Zhang

6610@cumt.edu.cn

https://orcid.org/0000-0002-6616-7626

Yuehong

Shenzhen Key Laboratory for Optimizing Design of Built Environment, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK School of Architecture and Design, China University of Mining and Technology, Xuzhou, 221000, China; Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK

05122025

1 141 151

4 September 2025

20 October 2025

5 December 2025

2025

This is an open access article under the CC BY 4.0 licence.

The dielectric crossed compound parabolic concentrator (dCCPC) exhibits significant potential for solar energy harvesting in photovoltaic applications and daylighting optimization in architectural design. Transmittance is a fundamental property that assesses the optical performance of dCCPC, which has been determined by ray-tracing simulation traditionally. However, this approach is computationally intensive and constrained by the sky models implemented in optical simulation tools. This study employed both multiple nonlinear regression (MNLR) and artificial neural network (ANN) models to predict dCCPC transmittance under all sky conditions including clear, intermediate and overcast skies. The high agreement of predicting results revealed the feasibility and accuracy of both methods, which achieved a coefficient of determination (R²) exceeding 0.93 and a mean square error (MSE) below 0.3%. Both methods offer the advantages of simplicity, speed, and accuracy for determining dCCPC's optical performance, while the choice between them should be based on specific practical requirements.

Dielectric crossed compound parabolic concentrator (dCCPC) Compound parabolic concentrator (CPC) Artificial neural network (ANN) Transmittance

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