A Resilient Ensemble Deep Learning Architecture for Load Forecasting Against FDI Attack

Short-term load forecasting (STLF) is crucial for ensuring power grid stability and economic dispatch. Its accuracy heavily depends on the quality of the input data. However, collecting operational data via the power system’s communication network poses a significant vulnerability to cyberattacks, particularly stealthy False Data Injection (FDI) attacks. By closely mimicking normal load fluctuations, these attacks evade conventional detection, thus, compromising forecasting reliability. To address this challenge, this paper proposes a novel resilient load forecasting framework that integrates two-stage attack detection with robust ensemble learning. In the detection stage, attack identification is performed through seasonal decomposition and AE-BiLSTM reconstruction, followed by restoration using periodic-consistent historical means and secondary screening via second-order differencing (SOD). In the forecasting stage, an improved Multi-Objective Whale Migration Algorithm (MO-WMA) is employed to adaptively optimize ensemble weights for intelligent fusion, significantly enhancing prediction accuracy and robustness, and providing a generalizable solution for intelligent grid load forecasting. Experiments were conducted on the Independent System Operator of New England (ISO New England, 2012–2014) load dataset under four typical FDI attack scenarios, with test sets including diverse attack intensities and temporal patterns. Results show that the framework achieves 98.98% attack detection accuracy and improves the R2 forecasting metric from 0.9053 to 0.9851, approaching attack-free performance, demonstrating effective recovery of forecasting accuracy and generalization capability.