2025-01-29 スイス連邦工科大学ローザンヌ校(EPFL)
<関連情報>
- https://actu.epfl.ch/news/new-study-improves-the-trustworthiness-of-wind-pow/
- https://www.sciencedirect.com/science/article/pii/S0306261924016568?via%3Dihub
風力発電予測における説明可能な人工知能は信頼できるか? Can we trust explainable artificial intelligence in wind power forecasting?
Wenlong Liao, Jiannong Fang, Lin Ye, Birgitte Bak-Jensen, Zhe Yang, Fernando Porte-Agel
Applied Energy Available online: 24 August 2024
DOI:https://doi.org/10.1016/j.apenergy.2024.124273
Highlights
- Four explainable artificial intelligence techniques are tailored to provide interpretability for machine learning models.
- Multiple metrics are defined to evaluate the trustworthiness of the interpretability.
- Explainable artificial intelligence techniques are extensively investigated on real datasets and machine learning models.
Abstract
Advanced artificial intelligence (AI) models typically achieve high accuracy in wind power forecasting, but their internal mechanisms lack interpretability, which undermines user confidence in forecast value and strategy execution. To this end, this paper aims to investigate the interpretability of AI models, which is crucial but usually overlooked in wind power forecasting. Specifically, four model-agnostic explainable artificial intelligence (XAI) techniques (i.e., Shapley additive explanations, permutation feature importance, partial dependence plot, and local interpretable model-agnostic explanations) are tailored to provide global and instance interpretability for AI models in wind power forecasting. Then, several metrics are proposed to evaluate the trustworthiness of interpretations provided by XAI techniques. Simulation results demonstrate that the proposed XAI techniques can not only identify important features from wind power datasets, but also enable the understanding of the contribution of each feature to the forecast power output for a specific sample. Furthermore, the proposed evaluation metrics aid users in comprehensively assessing the trustworthiness of XAI techniques in wind power forecasting, enabling them to judiciously select suitable XAI techniques for their AI models.