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

Eduardo Gomes, Augusto Esteves, Hugo Morais () and Lucas Pereira
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Eduardo Gomes: Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Augusto Esteves: Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Hugo Morais: Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Lucas Pereira: Instituto Superior Técnico—IST, Universidade de Lisboa, 1749-016 Lisboa, Portugal

Energies, 2025, vol. 18, issue 5, 1-17

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.

Keywords: explainable artificial intelligence; feature selection; machine learning; photovoltaic seasonality (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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