How machine learning can slash grid losses and boost renewables In smart grid management, ML enables dynamic control of distributed energy sources, managing real-time energy flows and detecting faults ...

The global energy grid is undergoing a profound transformation. Moving from traditional centralized control systems to more dynamic, distributed structures, this shift is particularly driven by the ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data.

In this paper, we propose a machine learning-based automated framework for algorithm selection and configuration for MPC applications. This framework aids the online implementation of MPC by selecting ...

This paper proposes a Random Forest grid fault prediction model based on Genetic Algorithm optimization (GA-RF) to classify the grid fault types, which improves the distribution network fault ...

The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network ...

The automatic induction of machine learning models capable of addressing supervised learning, feature selection, clustering, and reinforcement learning problems requires sophisticated intelligent ...