Attack-resilient framework for wind power forecasting against civil and adversarial attacks

Abstract

Forecasting wind power generation accurately is crucial for reliable, economical, and efficient integrations in smart grids, promoting applications of cleaner energy sources. Although effective wind power forecasting methods exist, power grids still require resilient schemes enabling accurate predictions under cyber-attacks. This paper introduces civil attack (CA) and fast gradient sign method (FGSM) attacks to wind power forecasting to analyze their impacts with countermeasures. The impacts of CA and FGSM attacks on a deep learning-based forecasting method are evaluated, finding FGSM attacks more severe. Also, an attack identification and corrupted data replacement-based pre-processing robust framework is proposed, outperforming other countermeasures. To detect and classify attacks, random forest (RF) has outperformed extreme gradient boosting (XGBoost), decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN). Experimental results on two different zones during CA and FGSM attacks indicate that the decrease in accuracy can be up to 0.4103, 0.3152, and 0.1683 in terms of root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE), respectively. The proposed framework successfully achieves an accuracy of 0.1204, 0.0835, and 0.0145 for the worst case in terms of RMSE, MAE, and MSE, respectively, signifying its importance for academic and industrial applications.