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<p>Ensuring predictive reliability and automation in smart grids is vital for stable and efficient electricity distribution. This study introduces the StarNet Ensemble Model, a web-based intelligent framework designed to enhance grid stability through a stacking-based ensemble learning approach. The system integrates a machine learning-driven graphical user interface (GUI) that enables automated, real-time monitoring and prediction of grid performance. A synthetic dataset, generated from a four-node star network and extended with consumer node variations, was initially used for model development and achieved a preliminary predictive accuracy of 99.43%, surpassing conventional methods. To extend applicability beyond simulated data, the model was validated using two benchmark datasets, namely the UCI Smart Grid Stability Dataset and the IEEE 14-Bus Test System. The StarNet model employed CatBoost, AdaBoost, Random Forest, SVM, and KNN as base learners with a Random Forest meta-model, evaluated through stratified 10-fold cross-validation. The model achieved 98.94% accuracy on the UCI dataset and 97.83% on the IEEE 14-Bus dataset, with a cross-dataset transfer accuracy of 95.41%. These results confirm the model’s robustness and generalization capability, demonstrating that the StarNet Ensemble Model effectively enhances predictive reliability and automation in smart grids.</p>

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Improving predictive reliability and automation of smart grids using the StarNet ensemble model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

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  • Amit Chhabra,
  • Sunil K. Singh,
  • Sudhakar Kumar,
  • Manraj Singh,
  • Utkarsh Chauhan,
  • Saksham Arora,
  • Wadee Alhalabi,
  • Varsha Arya,
  • Bassma Saleh Alsulami,
  • Ching-Hsien Hsu,
  • Brij B. Gupta

摘要

Ensuring predictive reliability and automation in smart grids is vital for stable and efficient electricity distribution. This study introduces the StarNet Ensemble Model, a web-based intelligent framework designed to enhance grid stability through a stacking-based ensemble learning approach. The system integrates a machine learning-driven graphical user interface (GUI) that enables automated, real-time monitoring and prediction of grid performance. A synthetic dataset, generated from a four-node star network and extended with consumer node variations, was initially used for model development and achieved a preliminary predictive accuracy of 99.43%, surpassing conventional methods. To extend applicability beyond simulated data, the model was validated using two benchmark datasets, namely the UCI Smart Grid Stability Dataset and the IEEE 14-Bus Test System. The StarNet model employed CatBoost, AdaBoost, Random Forest, SVM, and KNN as base learners with a Random Forest meta-model, evaluated through stratified 10-fold cross-validation. The model achieved 98.94% accuracy on the UCI dataset and 97.83% on the IEEE 14-Bus dataset, with a cross-dataset transfer accuracy of 95.41%. These results confirm the model’s robustness and generalization capability, demonstrating that the StarNet Ensemble Model effectively enhances predictive reliability and automation in smart grids.