.png){kind=icon}
返回
A Gradient-Based Wind Power Forecasting Attack Method Considering Point and Direction Selection
.png){kind=icon}
PRE
AI
DOI：10.1109/TSG.2023.3325390.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning methods have been prevailing in wind power forecasting, while these data-driven based methods are susceptible to cyberattacks. Typical attack methods inject malicious data into influence factors according to the gradient direction of the forecasting model to randomly increase or decrease forecasting results, ignoring the number of attacks and attack effect. In this paper, an attack sample selection model is proposed to select vulnerability sample points for attack in order to reduce the number of attacks. At the same time, an attack direction judgment model is developed to launch the attack in the correct gradient direction to maximize the attack effect. Moreover, the effectiveness of the proposed approach is validated on two public wind power datasets and nine typical machine learning based forecasting models such as ANN, ENN, RNN, LSTM, GRU, BiLSTM, BiGRU, CNN and TCN. Compared with the existing gradient-based attack methods, the proposed attack method increases MAPE values of the nine models by about 9% on average while improving the attack concealment.
Keyword:
Forecasting
Wind power generation
Predictive models
Wind speed
Load modeling
Data models
Wind farms
Wind power forecasting
machine learning
gradient-based attack
high-stealth attack
attack direction judgment
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
分享收藏分享收藏
分享收藏A review of wind speed and wind power forecasting with deep neural networks基于深度神经网络的风速和风功率预测研究综述.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
查看更多内容