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Implementation and validation of virtual clones of coloured building-integrated photovoltaic facades

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  • Manni, Mattia
  • Melkert, Tom
  • Lobaccaro, Gabriele
  • Jelle, Bjørn Petter

Abstract

A newly introduced colour correction transmittance factor (CCTF) and an innovative probabilistic-to-deterministic approach were applied to create virtual clones of coloured building-integrated photovoltaic (BIPV) systems. These virtual clones calculate the current at maximum power point (Impp) by adjusting the plane-of-array irradiance according to the transmittance properties of the coloured layer, which are governed by the CCTF. An ensemble of 200 randomly combined physical photovoltaic model chains was implemented (probabilistic approach), and the median of the diverse outputs was calculated to provide a deterministic Impp, estimations. The virtual clones were validated against observations from two BIPV facades located in Zwolle (The Netherlands), where black (CCTF=1.00), light-grey (CCTF=0.89), and terracotta (CCTF=0.70) photovoltaic modules were mounted. Hourly Impp data were collected from June 2023 to May 2024. The performance of different regression techniques was evaluated for the calibration of the virtual clones. The non-calibrated virtual clones showed similar accuracy throughout the year, with the determination coefficient (R2) that ranged from 0.594 (light-grey) to 0.613 (terracotta). Although the models generally overestimated Impp, the results demonstrated that such a tendency was accentuated during overcast days. Consistent biases were also observed for solar elevations greater than 30°. Finally, the façade orientation influenced the simulation performance. Indeed, the non-calibrated models overestimated by circa 150 A the annual Impp from the south-facing façade, and by more than 700 A the annual Impp from the façade oriented south-west, regardless of the colour. However, calibration, particularly with Random Forest and Gradient Boosting, consistently reduced cumulative error across all scenarios.

Suggested Citation

  • Manni, Mattia & Melkert, Tom & Lobaccaro, Gabriele & Jelle, Bjørn Petter, 2025. "Implementation and validation of virtual clones of coloured building-integrated photovoltaic facades," Applied Energy, Elsevier, vol. 378(PA).
  • Handle: RePEc:eee:appene:v:378:y:2025:i:pa:s0306261924022281
    DOI: 10.1016/j.apenergy.2024.124845
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    References listed on IDEAS

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    1. Hay, John E., 1993. "Calculating solar radiation for horizontal surfaces—I. Theoretically based approaches," Renewable Energy, Elsevier, vol. 3(4), pages 357-364.
    2. Hyunho Lee & Hyung‐Jun Song, 2021. "Current status and perspective of colored photovoltaic modules," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(6), November.
    3. Badescu, V., 2002. "3D isotropic approximation for solar diffuse irradiance on tilted surfaces," Renewable Energy, Elsevier, vol. 26(2), pages 221-233.
    4. Woo-Gyun Shin & Ju-Young Shin & Hye-Mi Hwang & Chi-Hong Park & Suk-Whan Ko, 2022. "Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning," Energies, MDPI, vol. 15(7), pages 1-17, April.
    5. Martina Pelle & Francesco Causone & Laura Maturi & David Moser, 2023. "Opaque Coloured Building Integrated Photovoltaic (BIPV): A Review of Models and Simulation Frameworks for Performance Optimisation," Energies, MDPI, vol. 16(4), pages 1-20, February.
    6. Olmo, F.J & Vida, J & Foyo, I & Castro-Diez, Y & Alados-Arboledas, L, 1999. "Prediction of global irradiance on inclined surfaces from horizontal global irradiance," Energy, Elsevier, vol. 24(8), pages 689-704.
    7. Gupta, Ruchi & Pena-Bello, Alejandro & Streicher, Kai Nino & Roduner, Cattia & Farhat, Yamshid & Thöni, David & Patel, Martin Kumar & Parra, David, 2021. "Spatial analysis of distribution grid capacity and costs to enable massive deployment of PV, electric mobility and electric heating," Applied Energy, Elsevier, vol. 287(C).
    8. Mayer, Martin János & Yang, Dazhi, 2023. "Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    9. Starke, Allan R. & Lemos, Leonardo F.L. & Barni, Cristian M. & Machado, Rubinei D. & Cardemil, José M. & Boland, John & Colle, Sergio, 2021. "Assessing one-minute diffuse fraction models based on worldwide climate features," Renewable Energy, Elsevier, vol. 177(C), pages 700-714.
    10. Every, Jeremy P. & Li, Li & Dorrell, David G., 2020. "Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations," Renewable Energy, Elsevier, vol. 147(P1), pages 2453-2469.
    11. Brito, M.C. & Freitas, S. & Guimarães, S. & Catita, C. & Redweik, P., 2017. "The importance of facades for the solar PV potential of a Mediterranean city using LiDAR data," Renewable Energy, Elsevier, vol. 111(C), pages 85-94.
    12. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    13. Abreu, Edgar F.M. & Canhoto, Paulo & Costa, Maria João, 2019. "Prediction of diffuse horizontal irradiance using a new climate zone model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 28-42.
    14. Starke, Allan R. & Lemos, Leonardo F.L. & Boland, John & Cardemil, José M. & Colle, Sergio, 2018. "Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction," Renewable Energy, Elsevier, vol. 125(C), pages 472-484.
    15. Hay, John E., 1993. "Calculating solar radiation for horizontal surfaces—II. Empirically based approaches," Renewable Energy, Elsevier, vol. 3(4), pages 365-372.
    16. Boland, John & Huang, Jing & Ridley, Barbara, 2013. "Decomposing global solar radiation into its direct and diffuse components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 749-756.
    17. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
    18. Gueymard, Christian A., 2014. "A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1024-1034.
    19. Hay, John E., 1993. "Calculating solar radiation for inclined surfaces: Practical approaches," Renewable Energy, Elsevier, vol. 3(4), pages 373-380.
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