Power Technology

Redefining load forecasting and management: how AI is making smart grids smarter

Legacy load forecasting models are struggling with ever-more-common, unpredictable events; power-hungry AI offers a solution.
IEEE

Self-Supervised Koopman Operator Learning for Distributed Final Synchronization Prediction of Networked Nonlinear Dynamics

Abstract: A hybrid Koopman deep learning algorithm is developed to predict the final synchronization of networked nonlinear dynamics with different topologies merely using neighboring state ...
GitHub

Centrum-IntelliPhysics/local-neural-operator-time-stepper-instead-of-just-time-stepper

This repository contains code for the paper: "Enabling Local Neural Operators to perform Equation-Free System-Level Analysis" G. Fabiani, H. Vandecasteele, S. Goswami, C. Siettos, I.G. Kevrekidis ...
IEEE

Performance-Oriented Data-Driven Control: Fusing Koopman Operator and MPC-Based Reinforcement Learning

Abstract: This letter develops the machinery of Koopman-based Model Predictive Control (KMPC) design, where the Koopman derived model is unable to capture the real nonlinear system perfectly. We then ...