Heinrich, René, Christoph Scholz, Stephan Vogt, and Malte Lehna. “Targeted Adversarial Attacks on Wind Power Forecasts”. Machine Learning 113, no. 2 (2023): 863–889. doi:10.1007/s10994-023-06396-9.
AbstractBibTeXEndNoteDOI
In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a Long Short-Term Memory (LSTM) network for predicting the power generation of individual wind farms and a Convolutional Neural Network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model's performance, as well as the extent to which the attacker's goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.
@article{heinrich2023targeted,
abstract = {In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a Long Short-Term Memory (LSTM) network for predicting the power generation of individual wind farms and a Convolutional Neural Network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model's performance, as well as the extent to which the attacker's goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.},
author = {Heinrich, René and Scholz, Christoph and Vogt, Stephan and Lehna, Malte},
journal = {Machine Learning},
keywords = {rl4ces},
number = 2,
pages = {863--889},
publisher = {Springer},
title = {Targeted Adversarial Attacks on Wind Power Forecasts},
volume = 113,
year = 2023
}
%0 Journal Article
%1 heinrich2023targeted
%A Heinrich, René
%A Scholz, Christoph
%A Vogt, Stephan
%A Lehna, Malte
%D 2023
%I Springer
%J Machine Learning
%N 2
%P 863--889
%R 10.1007/s10994-023-06396-9
%T Targeted Adversarial Attacks on Wind Power Forecasts
%V 113
%X In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a Long Short-Term Memory (LSTM) network for predicting the power generation of individual wind farms and a Convolutional Neural Network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model's performance, as well as the extent to which the attacker's goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.
URLBibTeXEndNoteDOILehna, Malte, Clara Holzhüter, Sven Tomforde, and Christoph Scholz. “HUGO – Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning With a Heuristic Target Topology Approach”. Sustainable Energy, Grids and Networks 39 (September 2024): 101510. doi:10.1016/j.segan.2024.101510.
URLBibTeXEndNoteDOIMussi, Marco, Gianvito Losapio, Alberto Maria Metelli, Marcello Restelli, Ricardo Bessa, Antoine Marot, Daniel Boos, et al. Position Paper on AI for the Operation of Critical Energy and Mobility Network Infrastructures. Porto: AI4REALNET, 2024. doi:https://irf.fhnw.ch/handle/11654/49377.
