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

Tian, Meng; Zhang, Li; Su, Yuehong

doi:10.61552/EC.2025.011

Energy Catalyst

Volume 1, 2025, Article 2025.011

Technical Article

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

, ,

¹Shenzhen 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

²School 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

³Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK

Published: 5 December 2025
Pages: 141-151
Received: 4 September 2025
Revised: 20 October 2025
Accepted: 5 December 2025
DOI: 10.61552/EC.2025.011
Access: Open access, CC BY 4.0

Published 5 December 2025Pages 141-151doi.org/10.61552/EC.2025.011

Received 4 Sep 2025Revised 20 Oct 2025Accepted 5 Dec 2025Open accessunder CC BY 4.0

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Abstract

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.

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

References

A. Kangazian and S. Z. Emadian Razavi, “Multi- criteria evaluation of daylight control systems of office buildings considering daylighting, glare and energy consumption,” Sol. Energy, vol. 263, p. 111928, 2023.
M. Hazbei, N. Rafati, N. Kharma, and U. Eicker, “Optimizing architectural multi-dimensional forms; a hybrid approach integrating approximate evolutionary search, clustering and local optimization,” Energy Buildings, vol. 318, p. 114460, 2024.
C. Reinhart and A. Fitz, “Findings from a survey on the current use of daylight simulations in building design,” Energy Buildings, vol. 38, pp. 824–835, 2006.
Y. Dan, M. Hu, Q. Wang, Y. Su, and S. Riffat, “Comprehensive evaluation of integrating radiative sky cooling with compound parabolic concentrator for cooling flux amplifying,” Energy, vol. 312, p. 133673, 2024.
F. Masood et al., “The compound parabolic concentrators for solar photovoltaic applications: Opportunities and challenges,” Energy Rep., vol. 8, pp. 13558–13584, 2022.
C. Jiang, L. Yu, S. Yang, K. Li, J. Wang, P. D. Lund, and Y. Zhang, “A Review of the Compound Parabolic Concentrator (CPC) with a Tubular Absorber,” Energies, vol. 13, p. 695, 2020.
D. Kumar et al., “Design and development of a novel and cost effective modified Compound parabolic trough collector,” Energy Convers. Manag., vol. 306, p. 118285, 2024.
T. Ulavi, T. Hebrink, and J. H. Davidson, “Analysis of a hybrid solar window for building integration,” Sol. Energy, vol. 105, pp. 290–302, 2014.
N. Sarmah and T. K. Mallick, “Design, fabrication and outdoor performance analysis of a low concentrating photovoltaic system,” Sol. Energy, vol. 112, pp. 361–372, 2015.
[10] N. Sarmah, B. S. Richards, and T. K. Mallick, “Design, development and indoor performance analysis of a low concentrating dielectric photovoltaic module,” Sol. Energy, vol. 103, pp. 390–401, 2014.
H. Baig, N. Sarmaah, D. Chemisana, J. Rosell, and T. K. Mallick, “Enhancing performance of a linear dielectric based concentrating photovoltaic system using a reflective film along the edge,” Energy, vol. 73, pp. 177–191, 2014.
W. T. Welford and R. Winston, The Optics of Nonimaging Concentrators: Light and Solar Energy. New York, NY, USA: Academic Press, 1978.
X. Yu, Y. Su, H. Zheng, and S. Riffat, “A study on use of miniature dielectric compound parabolic concentrator (dCPC) for daylighting control application,” Build. Environ., vol. 74, pp. 75–85, 2014.
M. Tian and Y. Su, “A study on use of three- dimensional miniature dielectric compound parabolic concentrator (3D dCPC) for daylighting control application,” in Proc. 14th Int. Conf. Sustain. Energy Technol. (SET 2015), Nottingham, UK, 2015.
M. Tian, L. Zhang, Y. Su, Q. Xuan, G. Li, and H. Lv, “An evaluation study of miniature dielectric crossed compound parabolic concentrator (dCCPC) panel as skylights in building energy simulation,” Sol. Energy, vol. 179, pp. 264–278, 2019.
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Lawrence Berkeley National Laboratory, Radiance. [Online]. Available: http://radsite.lbl.gov/radiance/framew.html. Accessed: Feb. 15, 2025.
Photopia User’s Guide. [Online]. Available: http://www.ltioptics.com/en/photopia-standalone-base.html. Accessed: Apr. 5, 2025.
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Q. Xuan et al., “Analysis and quantification of effects of the diffuse solar irradiance on the daylighting performance of the concentrating photovoltaic/daylighting system,” Build. Environ., vol. 193, p. 107654, 2021.
S. A. Waghmare and N. P. Gulhane, “Design configurations and possibilities of reflector shape for solar compound parabolic collector by ray tracing simulation,” Optik, vol. 176, pp. 315– 323, 2019.
S. Foster et al., “Assessment of the RACPC Performance under Diffuse Radiation for Use in BIPV System,” Appl. Sci., vol. 10, 2020.
X. Yu and Y. Su, “A discussion of inner south projection angle for performance analysis of dielectric compound parabolic concentrator,” Sol. Energy, vol. 113, pp. 101–113, 2015.
M. Tian and Y. Su, “Multiple nonlinear regression model for predicting the optical performances of dielectric crossed compound parabolic concentrator (dCCPC),” Sol. Energy, vol. 160, pp. 310–321, 2018.
W. Cao and C. Zhang, “An effective Parallel Integrated Neural Network System for industrial data prediction,” Appl. Soft Comput., vol. 107, p. 107397, 2021.
B. Roter, N. Ninkovic, and S. V. Dordevic, “Clustering superconductors using unsupervised machine learning,” Physica C: Supercond. Appl., vol. 598, p. 1354078, 2022.
R. Kumar, R. K. Aggarwal, and J. D. Sharma, “Comparison of regression and artificial neural network models for estimation of global solar radiations,” Renew. Sustain. Energy Rev., vol. 52, pp. 1294–1299, 2015.
W. Kim, Y. Jeon, and Y. Kim, “Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method,” Appl. Energy, vol. 162, pp. 666–674, 2016.
Y. Lei et al., “Precise lighting control via automated shading and artificial lighting: A systematic review,” Build. Environ., vol. 285, p. 113607, 2025.
F. Kotarela, A. Kyritsis, R. Agathokleous, and N. Papanikolaou, “On the exploitation of dynamic simulations for the design of buildings energy systems,” Energy, vol. 271, p. 127002, 2023.
J. Ngarambe, I. Adilkhanova, B. Uwiragiye, and G. Y. Yun, “A review on the current usage of machine learning tools for daylighting design and control,” Build. Environ., vol. 223, p. 109507, 2022.
S. G. Navada, C. S. Adiga, and S. G. Kimi, “Prediction of Daylight Availability for visual comfort,” Int. J. Appl. Eng. Res., vol. 11, pp. 4711– 4717, 2016.
N. K. Kandasamy, G. Karunagaran, C. Spanos, K. J. Tseng, and B.-H. Soong, “Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting,” Build. Environ., vol. 139, pp. 170–180, 2018.
A. Seyedolhosseini, N. Masoumi, M. Modarressi, and N. Karimian, “Daylight adaptive smart indoor lighting control method using artificial neural networks,” J. Build. Eng., vol. 29, p. 101141, 2020.
British Standards Institution, “Spatial distribution of daylight – CIE standard general sky,” BS ISO 15469:2004, 2004.
Illuminating Engineering Society of North America, Calculation of Daylight Availability (IES RP-21). New York: IES, 1984.
R. Perez, P. Ineichen, R. Seals, J. Michalsky, and R. Stewart, “Modeling daylight availability and irradiance components from direct and global irradiance,” Sol. Energy, vol. 44, pp. 271–289, 1990.
EnergyPlus Version 8.7 Documentation – Engineering Reference, U.S. Department of Energy, 2016.
I. Lou and Y. Zhao, “Sludge bulking prediction using principle component regression and artificial neural network,” Math. Probl. Eng., vol. 2012, p. 17, 2012.
J. Adamowski, H. F. Chan, S. O. Prasher, B. Ozga- Zielinski, and A. Sliusarieva, “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada,” Water Resour. Res., vol. 48, no. 1, 2012, Art. no. WR011913.
H. Shahzad, M. N. Sadiq, Z. Li, S. Algharni, T. Alqahtani, and K. Irshad, “Scientific computing of radiative heat transfer with thermal slip effects near stagnation point by artificial neural network,” Case Stud. Therm. Eng., vol. 54, p. 104024, 2024.
H. E. Abdellatif, S. A. Khan, A. Belaadi, and D. Ghernaout, “Enhancing thermal energy storage: The impact of inclined enclosure geometry and artificial neural network based modeling on phase change material melting performance,” J. Energy Storage, vol. 114, p. 115750, 2025.
H. Kumar C and R. P. Swamy, “Fatigue life prediction of glass fiber reinforced epoxy composites using artificial neural networks,” Compos. Commun., vol. 26, p. 100812, 2021.
T. G. H. Tipu, F. Yao, and T. Anwar, “Bifurcation, chaos, and soliton dynamics in the integrable space curve model: A hybrid analytical and artificial neural network approach,” Chaos Solitons Fractals, vol. 200, p. 117149, 2025.
MathWorks, Neural Network Toolbox (R2017a). [Online]. Available: https://cn.mathworks.com/help/nnet/index.ht ml. Accessed: Jul. 6, 2025.
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C. D. Lewis, Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. London, U.K.: Butterworth Scientific, 1982.
A. Rabl, J. O’Gallagher, and R. Winston, “Design and test of non-evacuated solar collectors with compound parabolic concentrators,” Sol. Energy, vol. 25, no. 4, pp. 335–351, 1980.
Y. Su, S. B. Riffat, and G. Pei, “Comparative study on annual solar energy collection of a novel lens- walled compound parabolic concentrator (lens- walled CPC),” Sustain. Cities Soc., vol. 4, pp. 35–40, 2012.
L. Le-Thanh, H. Nguyen-Thi-Viet, J. Lee, and H. Nguyen-Xuan, “Machine learning-based real- time daylight analysis in buildings,” J. Build. Eng., vol. 52, p. 104374, 2022.
T.-L. Le, H. Nguyen-Xuan, and S.-A. Kim, “Hand drawing-based daylight analysis using deep learning and augmented reality,” Results Eng., vol. 22, p. 102119, 2024.

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

, ,

³Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK

Published

5 December 2025

Pages

141-151

Received

4 September 2025

Revised

20 October 2025

Accepted

5 December 2025

DOI

10.61552/EC.2025.011

Access

Open access, CC BY 4.0

Published 5 December 2025Pages 141-151doi.org/10.61552/EC.2025.011

Received 4 Sep 2025Revised 20 Oct 2025Accepted 5 Dec 2025Open accessunder CC BY 4.0

Abstract

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

References

A. Kangazian and S. Z. Emadian Razavi, “Multi- criteria evaluation of daylight control systems of office buildings considering daylighting, glare and energy consumption,” Sol. Energy, vol. 263, p. 111928, 2023.
M. Hazbei, N. Rafati, N. Kharma, and U. Eicker, “Optimizing architectural multi-dimensional forms; a hybrid approach integrating approximate evolutionary search, clustering and local optimization,” Energy Buildings, vol. 318, p. 114460, 2024.
C. Reinhart and A. Fitz, “Findings from a survey on the current use of daylight simulations in building design,” Energy Buildings, vol. 38, pp. 824–835, 2006.
Y. Dan, M. Hu, Q. Wang, Y. Su, and S. Riffat, “Comprehensive evaluation of integrating radiative sky cooling with compound parabolic concentrator for cooling flux amplifying,” Energy, vol. 312, p. 133673, 2024.
F. Masood et al., “The compound parabolic concentrators for solar photovoltaic applications: Opportunities and challenges,” Energy Rep., vol. 8, pp. 13558–13584, 2022.
C. Jiang, L. Yu, S. Yang, K. Li, J. Wang, P. D. Lund, and Y. Zhang, “A Review of the Compound Parabolic Concentrator (CPC) with a Tubular Absorber,” Energies, vol. 13, p. 695, 2020.
D. Kumar et al., “Design and development of a novel and cost effective modified Compound parabolic trough collector,” Energy Convers. Manag., vol. 306, p. 118285, 2024.
T. Ulavi, T. Hebrink, and J. H. Davidson, “Analysis of a hybrid solar window for building integration,” Sol. Energy, vol. 105, pp. 290–302, 2014.
N. Sarmah and T. K. Mallick, “Design, fabrication and outdoor performance analysis of a low concentrating photovoltaic system,” Sol. Energy, vol. 112, pp. 361–372, 2015.
[10] N. Sarmah, B. S. Richards, and T. K. Mallick, “Design, development and indoor performance analysis of a low concentrating dielectric photovoltaic module,” Sol. Energy, vol. 103, pp. 390–401, 2014.
H. Baig, N. Sarmaah, D. Chemisana, J. Rosell, and T. K. Mallick, “Enhancing performance of a linear dielectric based concentrating photovoltaic system using a reflective film along the edge,” Energy, vol. 73, pp. 177–191, 2014.
W. T. Welford and R. Winston, The Optics of Nonimaging Concentrators: Light and Solar Energy. New York, NY, USA: Academic Press, 1978.
X. Yu, Y. Su, H. Zheng, and S. Riffat, “A study on use of miniature dielectric compound parabolic concentrator (dCPC) for daylighting control application,” Build. Environ., vol. 74, pp. 75–85, 2014.
M. Tian and Y. Su, “A study on use of three- dimensional miniature dielectric compound parabolic concentrator (3D dCPC) for daylighting control application,” in Proc. 14th Int. Conf. Sustain. Energy Technol. (SET 2015), Nottingham, UK, 2015.
M. Tian, L. Zhang, Y. Su, Q. Xuan, G. Li, and H. Lv, “An evaluation study of miniature dielectric crossed compound parabolic concentrator (dCCPC) panel as skylights in building energy simulation,” Sol. Energy, vol. 179, pp. 264–278, 2019.
MathWorks, MATLAB. [Online]. Available: http://uk.mathworks.com/index.html?s_tid=gn_logo. Accessed: Feb. 13, 2016.
Synopsys, LightTools Illumination Design Software. [Online]. Available: https://optics.synopsys.com/lighttools/lighttoo ls-product-literature.html. Accessed: Feb. 13, 2025.
Lawrence Berkeley National Laboratory, Radiance. [Online]. Available: http://radsite.lbl.gov/radiance/framew.html. Accessed: Feb. 15, 2025.
Photopia User’s Guide. [Online]. Available: http://www.ltioptics.com/en/photopia-standalone-base.html. Accessed: Apr. 5, 2025.
COMSOL AB, Introduction to COMSOL Multiphysics. [Online]. Available: https://www.comsol.com/comsol-multiphysics#overview. Accessed: Feb. 15, 2016.
Q. Xuan et al., “Analysis and quantification of effects of the diffuse solar irradiance on the daylighting performance of the concentrating photovoltaic/daylighting system,” Build. Environ., vol. 193, p. 107654, 2021.
S. A. Waghmare and N. P. Gulhane, “Design configurations and possibilities of reflector shape for solar compound parabolic collector by ray tracing simulation,” Optik, vol. 176, pp. 315– 323, 2019.
S. Foster et al., “Assessment of the RACPC Performance under Diffuse Radiation for Use in BIPV System,” Appl. Sci., vol. 10, 2020.
X. Yu and Y. Su, “A discussion of inner south projection angle for performance analysis of dielectric compound parabolic concentrator,” Sol. Energy, vol. 113, pp. 101–113, 2015.
M. Tian and Y. Su, “Multiple nonlinear regression model for predicting the optical performances of dielectric crossed compound parabolic concentrator (dCCPC),” Sol. Energy, vol. 160, pp. 310–321, 2018.
W. Cao and C. Zhang, “An effective Parallel Integrated Neural Network System for industrial data prediction,” Appl. Soft Comput., vol. 107, p. 107397, 2021.
B. Roter, N. Ninkovic, and S. V. Dordevic, “Clustering superconductors using unsupervised machine learning,” Physica C: Supercond. Appl., vol. 598, p. 1354078, 2022.
R. Kumar, R. K. Aggarwal, and J. D. Sharma, “Comparison of regression and artificial neural network models for estimation of global solar radiations,” Renew. Sustain. Energy Rev., vol. 52, pp. 1294–1299, 2015.
W. Kim, Y. Jeon, and Y. Kim, “Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method,” Appl. Energy, vol. 162, pp. 666–674, 2016.
Y. Lei et al., “Precise lighting control via automated shading and artificial lighting: A systematic review,” Build. Environ., vol. 285, p. 113607, 2025.
F. Kotarela, A. Kyritsis, R. Agathokleous, and N. Papanikolaou, “On the exploitation of dynamic simulations for the design of buildings energy systems,” Energy, vol. 271, p. 127002, 2023.
J. Ngarambe, I. Adilkhanova, B. Uwiragiye, and G. Y. Yun, “A review on the current usage of machine learning tools for daylighting design and control,” Build. Environ., vol. 223, p. 109507, 2022.
S. G. Navada, C. S. Adiga, and S. G. Kimi, “Prediction of Daylight Availability for visual comfort,” Int. J. Appl. Eng. Res., vol. 11, pp. 4711– 4717, 2016.
N. K. Kandasamy, G. Karunagaran, C. Spanos, K. J. Tseng, and B.-H. Soong, “Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting,” Build. Environ., vol. 139, pp. 170–180, 2018.
A. Seyedolhosseini, N. Masoumi, M. Modarressi, and N. Karimian, “Daylight adaptive smart indoor lighting control method using artificial neural networks,” J. Build. Eng., vol. 29, p. 101141, 2020.
British Standards Institution, “Spatial distribution of daylight – CIE standard general sky,” BS ISO 15469:2004, 2004.
Illuminating Engineering Society of North America, Calculation of Daylight Availability (IES RP-21). New York: IES, 1984.
R. Perez, P. Ineichen, R. Seals, J. Michalsky, and R. Stewart, “Modeling daylight availability and irradiance components from direct and global irradiance,” Sol. Energy, vol. 44, pp. 271–289, 1990.
EnergyPlus Version 8.7 Documentation – Engineering Reference, U.S. Department of Energy, 2016.
I. Lou and Y. Zhao, “Sludge bulking prediction using principle component regression and artificial neural network,” Math. Probl. Eng., vol. 2012, p. 17, 2012.
J. Adamowski, H. F. Chan, S. O. Prasher, B. Ozga- Zielinski, and A. Sliusarieva, “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada,” Water Resour. Res., vol. 48, no. 1, 2012, Art. no. WR011913.
H. Shahzad, M. N. Sadiq, Z. Li, S. Algharni, T. Alqahtani, and K. Irshad, “Scientific computing of radiative heat transfer with thermal slip effects near stagnation point by artificial neural network,” Case Stud. Therm. Eng., vol. 54, p. 104024, 2024.
H. E. Abdellatif, S. A. Khan, A. Belaadi, and D. Ghernaout, “Enhancing thermal energy storage: The impact of inclined enclosure geometry and artificial neural network based modeling on phase change material melting performance,” J. Energy Storage, vol. 114, p. 115750, 2025.
H. Kumar C and R. P. Swamy, “Fatigue life prediction of glass fiber reinforced epoxy composites using artificial neural networks,” Compos. Commun., vol. 26, p. 100812, 2021.
T. G. H. Tipu, F. Yao, and T. Anwar, “Bifurcation, chaos, and soliton dynamics in the integrable space curve model: A hybrid analytical and artificial neural network approach,” Chaos Solitons Fractals, vol. 200, p. 117149, 2025.
MathWorks, Neural Network Toolbox (R2017a). [Online]. Available: https://cn.mathworks.com/help/nnet/index.ht ml. Accessed: Jul. 6, 2025.
S. Haykin, Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1998.
C. D. Lewis, Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. London, U.K.: Butterworth Scientific, 1982.
A. Rabl, J. O’Gallagher, and R. Winston, “Design and test of non-evacuated solar collectors with compound parabolic concentrators,” Sol. Energy, vol. 25, no. 4, pp. 335–351, 1980.
Y. Su, S. B. Riffat, and G. Pei, “Comparative study on annual solar energy collection of a novel lens- walled compound parabolic concentrator (lens- walled CPC),” Sustain. Cities Soc., vol. 4, pp. 35–40, 2012.
L. Le-Thanh, H. Nguyen-Thi-Viet, J. Lee, and H. Nguyen-Xuan, “Machine learning-based real- time daylight analysis in buildings,” J. Build. Eng., vol. 52, p. 104374, 2022.
T.-L. Le, H. Nguyen-Xuan, and S.-A. Kim, “Hand drawing-based daylight analysis using deep learning and augmented reality,” Results Eng., vol. 22, p. 102119, 2024.

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

Abstract

References

How to cite this article

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

Abstract

References

How to cite this article