Deep learning-based probabilistic anomaly detection for solar forecasting under cyberattacks

Abstract

Recently, cyberattacks on solar power forecasting emerge with the increase of solar penetration, which may lead to substantial economic losses and power system reliability issues. This paper presents a novel probabilistic anomaly detection framework to effectively and accurately detect cyberattacks in solar power forecasting, which consists of three major components. First, a convolutional neural network is used to extract spatial correlations among solar farms at the bottom of the model. Second, a long short-term memory network is used to capture the temporal dependencies within solar power data and generate deterministic solar power forecasts, which is then converted to probabilistic forecasts through pinball loss optimization. Finally, the probabilistic solar power forecasts are used for anomaly detection. The effectiveness of the proposed framework is validated by using 16 solar farms with a variety of data integrity attacks. Numerical results of case studies show that the developed probabilistic anomaly detection methodology could effectively detect data integrity attacks in solar power forecasting with a relatively high accuracy.