IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v378y2025ipas0306261924022281.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924022281
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124845?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Badescu, V., 2002. "3D isotropic approximation for solar diffuse irradiance on tilted surfaces," Renewable Energy, Elsevier, vol. 26(2), pages 221-233.
    2. 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.
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. Hay, John E., 1993. "Calculating solar radiation for horizontal surfaces—II. Empirically based approaches," Renewable Energy, Elsevier, vol. 3(4), pages 365-372.
    9. 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.
    10. Hay, John E., 1993. "Calculating solar radiation for inclined surfaces: Practical approaches," Renewable Energy, Elsevier, vol. 3(4), pages 373-380.
    11. Hay, John E., 1993. "Calculating solar radiation for horizontal surfaces—I. Theoretically based approaches," Renewable Energy, Elsevier, vol. 3(4), pages 357-364.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manni, Mattia & Jouttijärvi, Sami & Ranta, Samuli & Miettunen, Kati & Lobaccaro, Gabriele, 2024. "Validation of model chains for global tilted irradiance on East-West vertical bifacial photovoltaics at high latitudes," Renewable Energy, Elsevier, vol. 220(C).
    2. Yang, Dazhi & Gu, Yizhan & Mayer, Martin János & Gueymard, Christian A. & Wang, Wenting & Kleissl, Jan & Li, Mengying & Chu, Yinghao & Bright, Jamie M., 2024. "Regime-dependent 1-min irradiance separation model with climatology clustering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    3. Rodríguez, Eduardo & Droguett, Enrique López & Cardemil, José M. & Starke, Allan R. & Cornejo-Ponce, Lorena, 2024. "Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models," Renewable Energy, Elsevier, vol. 231(C).
    4. Castillejo-Cuberos, A. & Cardemil, J.M. & Boland, J. & Escobar, R., 2024. "Irradiance separation model parameter estimation from historical cloud cover statistical properties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    5. Ruiz-Arias, José A. & Gueymard, Christian A., 2024. "Solar irradiance component separation benchmarking: The critical role of dynamically-constrained sky conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    6. 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).
    7. Wang, Nannan & Yue, Zijian & Liu, Yaolin & Liu, Yanfang, 2024. "Machine learning potentials for global multi-timescale diffuse irradiance estimation: Synthesizing ground observations, time-series, and environmental features," Energy, Elsevier, vol. 306(C).
    8. Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    9. Mayer, Martin János & Yang, Dazhi, 2022. "Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    10. Jasiewicz Jarosław & Cierniewski Jerzy, 2021. "SALBEC – A Python Library and GUI Application to Calculate the Diurnal Variation of the Soil Albedo," Quaestiones Geographicae, Sciendo, vol. 40(3), pages 95-107, September.
    11. 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).
    12. Kafka, Jennifer & Miller, Mark A., 2020. "The dual angle solar harvest (DASH) method: An alternative method for organizing large solar panel arrays that optimizes incident solar energy in conjunction with land use," Renewable Energy, Elsevier, vol. 155(C), pages 531-546.
    13. Mayer, Martin János & Yang, Dazhi, 2024. "Optimal place to apply post-processing in the deterministic photovoltaic power forecasting workflow," Applied Energy, Elsevier, vol. 371(C).
    14. Hay, John E., 1993. "Solar radiation data: Validation and quality control," Renewable Energy, Elsevier, vol. 3(4), pages 349-355.
    15. Karen Barbosa de Melo & Hugo Soeiro Moreira & Marcelo Gradella Villalva, 2020. "Influence of Solar Position Calculation Methods Applied to Horizontal Single-Axis Solar Trackers on Energy Generation," Energies, MDPI, vol. 13(15), pages 1-15, July.
    16. 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.
    17. Chu, Yinghao & Yang, Dazhi & Yu, Hanxin & Zhao, Xin & Li, Mengying, 2024. "Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?," Applied Energy, Elsevier, vol. 356(C).
    18. Verena Weiler & Jonas Stave & Ursula Eicker, 2019. "Renewable Energy Generation Scenarios Using 3D Urban Modeling Tools—Methodology for Heat Pump and Co-Generation Systems with Case Study Application †," Energies, MDPI, vol. 12(3), pages 1-19, January.
    19. Ailton M. Tavares & Ricardo Conceição & Francisco M. Lopes & Hugo G. Silva, 2022. "Development of a Simple Methodology Using Meteorological Data to Evaluate Concentrating Solar Power Production Capacity," Energies, MDPI, vol. 15(20), pages 1-27, October.
    20. Chinchilla, Monica & Santos-Martín, David & Carpintero-Rentería, Miguel & Lemon, Scott, 2021. "Worldwide annual optimum tilt angle model for solar collectors and photovoltaic systems in the absence of site meteorological data," Applied Energy, Elsevier, vol. 281(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:378:y:2025:i:pa:s0306261924022281. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.