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SS03

SS03: Advances in Machine Learning for Photovoltaic System Optimization and Control in Modern Energy Grids

Peter Glösekötter

FH Münster, Germany

Joseph Moerschell

Haute École Spécialisée de Suisse Occidentale, Switzerland

Ignacio Rojas

Universidad de Granada, Spain

Tilman Sanders

FH Münster, Germany

Markus Gregor

FH Münster, Germany

Sarah Trinschek

FH Münster, Germany

Catalin Stoean

University of Craiova, Romania

Abstract

With the increasing integration of renewable energy sources like photovoltaics (PV) into modern energy systems, achieving grid efficiency, while maintaining stability, have become complex challenges, especially due to the variable nature of PV power generation. Machine learning (ML) provides promising solutions for forecasting, monitoring, and optimizing energy flow and control to maximize PV utilization, but still ensuring grid stability. This session aims to bring together researchers and practitioners to discuss emerging ML techniques specifically designed for optimizing photovoltaic systems and sensors in the context of modern energy grids.

Topics of interest include (but are not limited to):

  • PV production forecasting using time series analysis
  • Sky-camera image processing
  • Estimation of grid-connected storage state through ML
  • Sensor placement optimization through machine learning
  • Fault detection and stability analysis in power grids using ML
  • Optimizing power systems through metaheuristics
  • Explainable AI models for energy system management
  • End-to-end ML pipelines for PV fault detection and maintenance planning