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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Cyber-resilient machine learning framework for accurate individual load forecasting and anomaly detection in smart

M Tayseer1, M Talaat2,3, Amr A Zamel4,5

  • 1Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, P.O. 44519, Zagazig, Egypt.

Scientific Reports
|December 17, 2025
PubMed
Summary

This study introduces a KMEANS-NN model for accurate electricity load forecasting in smart grids. The cyber-resilient system enhances prediction accuracy and detects cyber-attacks, reducing computational time significantly.

Keywords:
Anomaly detection scheme (ADS)Cyber-attack scenariosK-MEANS clustering and neural networks (KMEANS–NN)Load forecastingResilient support vector machineSmart meters

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Smart grid evolution necessitates accurate and secure electricity load predictions for efficient energy management.
  • Individual Load Forecasting (ILF) is critical for smart grid reliability and operational efficiency.
  • Cyber-security threats, such as false data injection attacks, pose risks to smart grid integrity.

Purpose of the Study:

  • To propose a scalable and cyber-resilient methodology for electricity consumption forecasting at the individual smart meter level.
  • To enhance Individual Load Forecasting (ILF) accuracy and reduce computational complexity using machine learning.
  • To implement an Anomaly Detection Scheme (ADS) for identifying cyber-attacks in smart meter data.

Main Methods:

  • Utilized K-MEANS Clustering and Neural Networks (KMEANS-NN) for enhanced ILF.
  • Employed Principal Component Analysis based One-Class Support Vector Machine (PCA-OCSVM) as an Anomaly Detection Scheme (ADS).
  • Validated the integrated model using five months of real-world smart meter data from Egypt, including simulated cyber-attacks.

Main Results:

  • KMEANS-NN significantly reduced Mean Absolute Adjusted Percentage Error (MAAPE) by up to 40% and computational time from days to minutes.
  • The proposed Anomaly Detection Scheme (ADS) achieved high accuracy (99.9%), sensitivity (99.8%), precision (99.9%), specificity (99.9%), and F1-score (99.8%) in detecting cyber-attacks.
  • The integrated model demonstrated improved forecasting accuracy across various benchmark models (ARIMA, CTREE, MLP, NNETAR).

Conclusions:

  • The integrated KMEANS-NN and PCA-OCSVM model offers a scalable, accurate, and cyber-resilient solution for electricity load forecasting in smart grids.
  • The methodology significantly enhances forecasting performance and provides robust protection against cyber-attacks.
  • The proposed system shows strong potential for deployment in large-scale smart grid environments, improving energy management and reliability.