Explainability of a Deep Neural Network Model for Prediction of Solar Panels Generation: Comparative Study
DOI:
https://doi.org/10.14313/jamris-2026-008Keywords:
LIME, SHAP, Integrated Gradients, Saliency MapAbstract
The explainability methods LIME, SHAP, Integrated Gradients and Sailency Maps were compared to explain the predictions of a deep learning model trained to predict the electricity generation of a photovoltaic park.
These methods allow analyzing the relative importance of the input features for each prediction.
The quality of the explanations generated was evaluated using the fidelity and continuity metrics, before and after applying perturbations to the data.
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Copyright (c) 2026 Rosalís Amador García, María Matilde García Lorenzo, Rafael E. Bello Pérez
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors retain copyright. Authors grant the journal a non-exclusive right to publish the article. Articles are published under the CC BY-NC-ND 4.0 licence.
