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Leveraging Explainable Artificial Intelligence in Solar Photovoltaic Mappings: Model Explanations and Feature Selection

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  • Eduardo Gomes

    (Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal
    INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigacão e Desenvolvimento, 1000-029 Lisboa, Portugal
    ITI/LARSyS—Interactive Technologies Institute, 1900-319 Lisboa, Portugal)

  • Augusto Esteves

    (Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal
    ITI/LARSyS—Interactive Technologies Institute, 1900-319 Lisboa, Portugal)

  • Hugo Morais

    (Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal
    INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigacão e Desenvolvimento, 1000-029 Lisboa, Portugal)

  • Lucas Pereira

    (Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal
    ITI/LARSyS—Interactive Technologies Institute, 1900-319 Lisboa, Portugal)

Abstract

This work explores the effectiveness of explainable artificial intelligence in mapping solar photovoltaic power outputs based on weather data, focusing on short-term mappings. We analyzed the impact values provided by the Shapley additive explanation method when applied to two algorithms designed for tabular data—XGBoost and TabNet—and conducted a comprehensive evaluation of the overall model and across seasons. Our findings revealed that the impact of selected features remained relatively consistent throughout the year, underscoring their uniformity across seasons. Additionally, we propose a feature selection methodology utilizing the explanation values to produce more efficient models, by reducing data requirements while maintaining performance within a threshold of the original model. The effectiveness of the proposed methodology was demonstrated through its application to a residential dataset in Madeira, Portugal, augmented with weather data sourced from SolCast.

Suggested Citation

  • Eduardo Gomes & Augusto Esteves & Hugo Morais & Lucas Pereira, 2025. "Leveraging Explainable Artificial Intelligence in Solar Photovoltaic Mappings: Model Explanations and Feature Selection," Energies, MDPI, vol. 18(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1282-:d:1606218
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    References listed on IDEAS

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