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Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods

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)

  • Miguel Alonso-Abella

    (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)

  • José Cuenca

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

  • Marina de la Cruz

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

Abstract

Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly based on PV time series has some advantages over solar irradiance forecasting first and PV power modeling afterwards. In this paper, the power forecasting for BIPV systems in a vertical façade is studied using machine learning algorithms based on decision trees. The forecasting scheme employs the skforecast library from the Python environment, which facilitates the implementation of different schemes for both deterministic and probabilistic forecasting applications. Firstly, deterministic forecasting of hourly BIPV power was performed with XGBoost and Random Forest algorithms for different cases, showing an improvement in forecasting accuracy when some exogenous variables were used. Secondly, probabilistic forecasting was performed with XGBoost combined with the Bootstrap method. The results of this paper show the capabilities of Random Forest and gradient boosting algorithms, such as XGBoost, to work as regressors in time series forecasting of BIPV power. Mean absolute error in the deterministic forecast, using the most influencing exogenous variables, were around 40% and close below 30% for the south and east array, respectively.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1495-:d:1055966
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    References listed on IDEAS

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    1. 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.
    2. Nuria Martín-Chivelet & Jesús Polo & Carlos Sanz-Saiz & Lucy Tamara Núñez Benítez & Miguel Alonso-Abella & José Cuenca, 2022. "Assessment of PV Module Temperature Models for Building-Integrated Photovoltaics (BIPV)," Sustainability, MDPI, vol. 14(3), pages 1-15, January.
    3. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
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    Cited by:

    1. Domenico Palladino & Nicolandrea Calabrese, 2023. "Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector," Energies, MDPI, vol. 16(7), pages 1-28, March.
    2. Fouzi Harrou & Ying Sun & Bilal Taghezouit & Abdelkader Dairi, 2023. "Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting," Energies, MDPI, vol. 16(18), pages 1-5, September.

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