no code implementations • 12 Mar 2024 • Simon Letzgus, Klaus-Robert Müller, Grégoire Montavon
In regression, explanations need to be precisely formulated to address specific user queries (e. g.\ distinguishing between `Why is the output above 0?'
1 code implementation • 19 Apr 2023 • Simon Letzgus, Klaus-Robert Müller
Alongside this paper, we publish a Python implementation of the presented framework and hope this can guide researchers and practitioners alike toward training, selecting and utilizing more transparent and robust data-driven wind turbine power curve models.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
no code implementations • 21 Oct 2022 • Simon Letzgus
Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
1 code implementation • 21 Dec 2021 • Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, Gregoire Montavon
In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks.
1 code implementation • Wind Energy Science 2020 • Simon Letzgus
For an automated change-point-free sequence selection, the most severe 60 % of all change points (CPs) could be automatically removed with a precision of more than 0. 96 and therefore without any significant loss of training data.