IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i11p4173-d832707.html
   My bibliography  Save this article

BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin

Author

Listed:
  • Jesús Polo

    (Photovoltaic Solar Energy Unit (Renewable Energy Division, CIEMAT), Avda. Complutense 40, 28040 Madrid, Spain)

  • Nuria Martín-Chivelet

    (Photovoltaic Solar Energy Unit (Renewable Energy Division, CIEMAT), Avda. Complutense 40, 28040 Madrid, Spain)

  • Carlos Sanz-Saiz

    (Photovoltaic Solar Energy Unit (Renewable Energy Division, CIEMAT), Avda. Complutense 40, 28040 Madrid, Spain)

Abstract

Modeling the photovoltaic (PV) energy output with high accuracy is essential for predicting and analyzing the performance of a PV system. In the particular cases of building-integrated and building-attached photovoltaic systems (BIPV and BAPV, respectively) the time-varying partial shading conditions are a relevant added difficulty for modeling the PV power conversion. The availability of laser imaging detection and ranging (LIDAR) data to create very-high-resolution elevation digital models can be effectively used for computing the shading at high resolution. In this work, an artificial neural network (ANN) has been used to model the power generation of different BIPV arrays on a 5 min basis using the meteorological and solar irradiance on-site conditions, as well as the shading patterns estimated from a digital surface model as inputs. The ANN model has been validated using three years of 5-min-basis monitored data showing very high accuracy (6–16% of relative error depending on the façade). The proposed methodology combines the shading computation from a digital surface model with powerful machine learning algorithms for modeling vertical PV arrays under partial shading conditions. The results presented here prove also the capability of the machine learning techniques towards the creation of a digital twin for the specific case of BIPV systems that complements the conventional monitoring strategies and can be used in the diagnosis of performance anomalies.

Suggested Citation

  • Jesús Polo & Nuria Martín-Chivelet & Carlos Sanz-Saiz, 2022. "BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin," Energies, MDPI, vol. 15(11), pages 1-11, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4173-:d:832707
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/11/4173/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/11/4173/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
    3. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    4. Celik, Berk & Karatepe, Engin & Silvestre, Santiago & Gokmen, Nuri & Chouder, Aissa, 2015. "Analysis of spatial fixed PV arrays configurations to maximize energy harvesting in BIPV applications," Renewable Energy, Elsevier, vol. 75(C), pages 534-540.
    5. Freitas, Jader de Sousa & Cronemberger, Joára & Soares, Raí Mariano & Amorim, Cláudia Naves David, 2020. "Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug," Renewable Energy, Elsevier, vol. 160(C), pages 1468-1479.
    6. Nuria Martín-Chivelet & Juan Carlos Gutiérrez & Miguel Alonso-Abella & Faustino Chenlo & José Cuenca, 2018. "Building Retrofit with Photovoltaics: Construction and Performance of a BIPV Ventilated Façade," Energies, MDPI, vol. 11(7), pages 1-15, July.
    7. Dorian Esteban Guzman Razo & Björn Müller & Henrik Madsen & Christof Wittwer, 2020. "A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning," Energies, MDPI, vol. 13(24), pages 1-20, December.
    8. Almonacid, F. & Rus, C. & Pérez, P.J. & Hontoria, L., 2009. "Estimation of the energy of a PV generator using artificial neural network," Renewable Energy, Elsevier, vol. 34(12), pages 2743-2750.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jesús Polo & Nuria Martín-Chivelet & Miguel Alonso-Abella & Carlos Sanz-Saiz & José Cuenca & Marina de la Cruz, 2023. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods," Energies, MDPI, vol. 16(3), pages 1-12, February.
    2. Kenji Araki & Yasuyuki Ota & Akira Nagaoka & Kensuke Nishioka, 2023. "3D Solar Irradiance Model for Non-Uniform Shading Environments Using Shading (Aperture) Matrix Enhanced by Local Coordinate System," Energies, MDPI, vol. 16(11), pages 1-20, May.

    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. Kenji Araki & Yasuyuki Ota & Akira Nagaoka & Kensuke Nishioka, 2023. "3D Solar Irradiance Model for Non-Uniform Shading Environments Using Shading (Aperture) Matrix Enhanced by Local Coordinate System," Energies, MDPI, vol. 16(11), pages 1-20, May.
    2. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    3. Han, Youhua & Liu, Yang & Lu, Shixiang & Basalike, Pie & Zhang, Jili, 2021. "Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions," Energy, Elsevier, vol. 237(C).
    4. Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Models for the electrical characterization of high concentration photovoltaic cells and modules: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 752-760.
    5. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    6. Fernández, Eduardo F. & Almonacid, Florencia, 2014. "Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications," Energy, Elsevier, vol. 74(C), pages 941-949.
    7. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    8. Almonacid, F. & Fernández, E.F. & Mallick, T.K. & Pérez-Higueras, P.J., 2015. "High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature," Energy, Elsevier, vol. 84(C), pages 336-343.
    9. Jimyung Kang & Jooseung Lee & Soonwoo Lee, 2023. "Data-Driven Minute-Ahead Forecast of PV Generation with Adjacent PV Sector Information," Energies, MDPI, vol. 16(13), pages 1-16, June.
    10. Minli Wang & Peihong Wang & Tao Zhang, 2022. "Evidential Extreme Learning Machine Algorithm-Based Day-Ahead Photovoltaic Power Forecasting," Energies, MDPI, vol. 15(11), pages 1-25, May.
    11. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    12. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    13. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    14. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
    15. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    16. Sommerfeldt, Nelson & Madani, Hatef, 2017. "Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part one – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1379-1393.
    17. Manuel Angel Gadeo-Martos & Antonio Jesús Yuste-Delgado & Florencia Almonacid Cruz & Jose-Angel Fernandez-Prieto & Joaquin Canada-Bago, 2019. "Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems," Energies, MDPI, vol. 12(3), pages 1-22, February.
    18. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
    19. Rehman, Shafiqur & El-Amin, Ibrahim, 2012. "Performance evaluation of an off-grid photovoltaic system in Saudi Arabia," Energy, Elsevier, vol. 46(1), pages 451-458.
    20. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(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:gam:jeners:v:15:y:2022:i:11:p:4173-:d:832707. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.