Search Results for author: Simon Letzgus

Found 5 papers, 3 papers with code

XpertAI: uncovering model strategies for sub-manifolds

no code implementations12 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?'

regression

An XAI framework for robust and transparent data-driven wind turbine power curve models

1 code implementation19 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

XAI for transparent wind turbine power curve models

no code implementations21 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

Toward Explainable AI for Regression Models

1 code implementation21 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.

Explainable Artificial Intelligence (XAI) regression

Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models

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.

Change Point Detection Semi-supervised Anomaly Detection +1

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