Cyber-resilient machine learning framework for accurate individual load forecasting and anomaly detection in smart grids

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With the evolution of smart grids, accurate and secure predictions of the electricity load become crucial for efficient energy management and reliability. In this paper, a scalable and cyber-resilient methodology for electricity consumption forecasting on individual smart meter level based on machine learning and anomaly detection schemes is proposed. The proposed technique utilizes K-MEANS Clustering and Neural Networks (KMEANS-NN) to enhance Individual Load Forecasting (ILF) with reduced computational complexity and high prediction accuracy. A Principal Component Analysis based One-Class Support Vector Machine (PCA-OCSVM) model is employed as an Anomaly Detection Scheme (ADS) to identify the false data injection attacks in smart meter telemetry. The system uses five months of real-world data from [Formula: see text] smart meters gathered under the supervision of Electrical Distribution Sector (EDS) of Suez Canal Authority (SCA) in Egypt. KMEANS-NN strategy reduces significantly MAAPE by up to [Formula: see text] and cuts computational time from days to minutes. It improves forecasting accuracy across four proposed models: ARIMA, CTREE, MLP and NNETAR. To assess the cyber-security profile, [Formula: see text] of the dataset is orchestrated with scaling, ramping and random cyber-attack simulation. Proposed ADS achieves [Formula: see text] overall accuracy, [Formula: see text] sensitivity, [Formula: see text] precision, [Formula: see text] specificity and F1-score of[Formula: see text], whereas it's [Formula: see text] accurate on clean data. This integrated model offers accurate, efficient, and secure load forecasting presenting good potential for its deployment in large-scale smart grid environments.

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