Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-31479-z