Machine Learning for Sustainable Power Systems
ECML 2025 Workshop
Porto, on the 15th September 2025
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Workshop Objective
Workshop Objective
Introduction
The workshop ML4SPS focuses on, but is not limited to, several key topics within the energy system, as listed below. However, contributions to other related research areas are also welcome. In terms of methods and algorithmic approaches, the workshop does not limit any contributions if they belong to the field of machine learning.
Forecasting in energy systems
Advanced predictive models for forecasting of electricity demand, wind energy, and solar/PV power generation using deep learning and/or time series analysis.
Ensemble methods and uncertainty quantification in energy forecasting to enhance decision-making processes.
Grid Control and Optimization:
Machine learning algorithms for real-time grid management and operation, including load balancing, outage management, frequency regulation, grid calculations and voltage control.
Integration of distributed energy resources (DERs) and energy storage solutions through intelligent control systems.
Approaches on generalizability und adaptability of power system applications
Asset Management and Predictive Maintenance:
Predictive analytics for asset health monitoring, leveraging IoT data, forecasting equipment failures.
Machine learning frameworks for life-cycle management of energy infrastructure, enhancing reliability and optimizing operational costs.
Techniques for real-time anomaly detection and diagnosis in power systems.
Energy Markets and Trading Strategies:
Application of machine learning in analysing energy market dynamics, price forecasting, and risk assessment
AI-driven tools for optimizing energy procurement, portfolio management, and demand-side response.
Impact analysis of renewable energy penetration on energy markets.
Energy Integration and Planning:
Optimization models for the effective integration of renewable energy source
Simulation and scenario analysis AI-tools with respect to grid planning, renewable energy deployment and infrastructure resilience
Integration of Electric Vehicles into the Power Grid:
Machine learning for managing the impact of electric vehicle charging on the power grid.
Smart charging algorithms and strategies for electric vehicles.
Ethical AI and Socioeconomic Impacts of Machine Learning in Power Systems:
Ethical considerations and governance frameworks for the deployment of AI in energy systems
Machine learning's role in addressing energy poverty and ensuring equitable access to clean energy.
Robustness in Industrial AI Applications:
Methods to enhance the robustness of machine learning models against adversarial attacks, data corruption, and model drift in energy systems.
Strategies ensuring transparency in AI-based decision-making, including explainable AI (XAI) techniques for energy applications.
We encourage submissions related to critical infrastructures other than power systems to be directed to the AI for Safety-Critical Infrastructures (AI-SCI) workshop, which is organized in the context of the AI4REALNET project, provided they align with its scope.
Workshop Program
Workshop Program
Call for Papers
Call for Papers
Introduction
Integrating large amounts of renewable energy into the energy system is a major challenge. Unlike traditional energy production methods, renewable energy (wind and solar) is highly variable, decentralized and only partially predictable. In addition, energy consumption is changing rapidly with the increasing use of electricity in heavy industries and electric cars. This variability can lead to imbalances between energy supply and demand, which can strain the grid and increase the risk of blackouts.
Machine Learning (ML) has emerged as a promising tool for addressing the challenges of integrating renewable energy into energy systems. ML algorithms can be used in various fields of to improve the forecasting accuracy of renewable energy production, optimize energy planning and network control, enable predictive maintenance of energy infrastructure, design efficient energy markets or assess the variability of the energy consumption. However, in the future use of possible ML algorithms in energy systems, it is crucial to consider robustness and to ensure confidence in their results, as the models will be used in critical infrastructures. Therefore, trust for industrial ML methods needs to be considered to ensure reliable and effective models.
Workshop Objectives
The Machine Learning for Sustainable Power Systems workshop aims to bring together researchers and practitioners in the fields of machine learning and energy systems to discuss the latest advancements in machine learning for sustainable power systems applications. The workshop will provide a platform for researchers to present their work, exchange ideas, and network with other experts in the field.
Target Audience
The workshop welcomes submissions from researchers, practitioners, and PhD students working in the areas of machine learning, energy systems, and sustainable energy. Papers are encouraged from a variety of perspectives, including:
Forecasting in energy systems, e.g., demand, wind and solar forecasting
Grid control, grid analysis and optimization
Asset management and predictive maintenance
Energy Markets and trading strategies
Energy integration and planning
Integration of electric vehicles into the power grid
Ethical AI and socioeconomic impacts of ML in power systems
Robustness for industrial AI applications
Contributions more related to Trust and AI-Human collaboration will be encouraged to be submitted to the AI4RealNet workshop, provided they align with its scope.
Submission Guidelines
Authors are invited to submit original papers (maximum 16 pages for technical papers, maximum 8 pages for short position papers) describing their research on machine learning applications for sustainable power systems. Papers should be submitted electronically in PDF format by 14 June 2025 and authors are encouraged to include open-source code with their work. Papers must be formatted according to the ECML guidelines. Further information can be found on the ECML website.
The Workshops and Tutorials will be included in a joint Post-Workshop proceeding published by Springer Communications in Computer and Information Science, in 1-2 volumes, organized by focused scope. Papers authors will have the faculty opt-in or opt-out. We suggest that workshop papers be prepared and submitted in the LNCS format: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Paper Evaluation
Submitted papers will be reviewed by a panel of experts in the fields of machine learning and power systems. Papers will be evaluated based on their innovativeness, technical accuracy, and potential to advance the field. Successful submissions will be presented at the workshop and published on the ML4SPS website.
Important Dates
Paper submission deadline: 14 June 2025 30 June 2025
Notification of acceptance: 14 July 2025 22 July 2025 24 July 2025
Camera Ready Version unitl: 05. Sept 2025 19 Sept 2025
Submit via Microsoft CMT
https://cmt3.research.microsoft.com/ECMLPKDDWorkshopTrack2025/Track/13/Submission/Create
Please be sure to select ML4SPS from the drop down menu when creating your submission. Authors who submit their work to ML4SPS 2025 commit themselves to present their paper in person at the workshop in Porto in case of acceptance (at least one author).
Keynote Speakers
Keynote Speakers
Pierre Pinson
Pierre Pinson is the Chair of Data-centric Design Engineering at Imperial College London, Dyson School of Design Engineering, as well as the Deputy-head of School. He is a Chief Scientist at Halfspace, part of Accenture, as well as an affiliated Professor of Operations Research at the Technical University of Denmark, Department of Technology, Management and Economics. He is an international research fellow at the CoRE centre of Aarhus University in Denmark. He is the Editor-in-Chief of the International Journal of Forecasting, the leading scientific journal in the science and applications of forecasting. He is an IEEE Fellow, as well as an INFORMS member and an IIF director. He is on the Highly-cited Researcher list of WoS/Clarivate over 2019-2023 (cross-field category) for numerous high-impact works in statistics/OR, meteorology, economics and power/energy engineering. He is seen as a leading figure internationally within predictive analytics. He has received the INFORMS Franz Edelman Award 2024 for outstanding achievements within operations research, analytics and management science.
He has been a guest researcher at the University of Oxford, Mathematical Institute, at the University of Washington (Seattle), Department of Statistics, and a visiting professor at the Ecole Normale Supérieure (Rennes, France). In addition, he had a one-year stay as a scientist at the European Centre for Medium-range Weather Forecasts (ECMWF, located in Reading, UK), the world-leading organization in global weather forecasting research and operational services. In 2019, he was a Simons fellow at the Isaac Newton Institute for Mathematical Sciences, Cambridge, UK.
For more information visit http://pierrepinson.com
Keynote: AI at the core of electricity markets
Adrian Kelly
Adrian Kelly is a Senior Principal Project Manager at EPRI Europe working in the Grid Operations & Planning team.
He leads research and projects in EPRI in the area of transmission real-time operations situational awareness, in particular developing research for the control center of the future. His research interests and projects include HMI and display design, alarm management and artificial intelligence application in real-time system control, operations security standards and the interface between the real-time system and protection. He was a key member of the research group developing the Learning to Run a Power Network (L2RPN) challenge working towards a reinforcement learning based co-pilot for network operators. He has developed a framework for developing AI/ML use cases for the transmission sector and leads the AI.Grid Interest Group at EPRI, including the development of the AI/ML Use Case Register.
Previously, Adrian worked for 9 years in the Operations and Planning departments of EirGrid, the transmission system operator in Ireland, including working as a real time grid, balancing and market operator.
Adrian received a Bachelor of Engineering (Electrical) from University College Dublin in 2007. He is a chartered (professional) engineer with Engineers Ireland and is an active member of CIGRE and IEEE. He lives in Dublin with his wife and two children.
Keynote: Climbing the Decision Ladder: Can RL‑Guided LLMs Rewire Power System Decision‑Making?
Workshop Chairs
Workshop Chairs
Organization Committee
Malte Lehna
Fraunhofer Institute for Energy Economics and Energy System Technology IEE
Clara Holzhüter
Intelligent Embedded Systems, University of Kassel
Clément Goubet
Data Science and AI Team, RTE, France's Transmission System Operator
Dr. Christoph Scholz
Fraunhofer Institute for Energy Economics and Energy System Technology IEE
Intelligent Embedded Systems, University of Kassel
Dr. Benjamin Donnot
L2RPN and Grid2OP initiator
RTE, France's Transmission System Operator
Mohamed Hassouna
Intelligent Embedded Systems, University of Kassel
Program Committee
The international program committee for the workshop Machine Learning for Sustainable Power Systems comprises prominent experts from the research field of energy systems as well as state-of-the-art machine learning domains. This ensures a thorough and equitable reviewing process.
Alessandro Zocca: Department of Mathematics - Vrije Universiteit Amsterdam
Alexander Scheidler: Grid Operation Management - Fraunhofer IEE
Benjamin Donnot: Data Science and AI tech lead - RTE
Benjamin Säfken: Institute of Mathmatics - TU Clausthal
Björn Hoppmann: Cognitive Energy Systems - Fraunhofer IEE
Catalina Obando: Data Science and AI team member - RTE
Chandana Priya Nivarthi: Autonomous Energy Systems - Uni Kassel
Connor Schönberner: Intelligent Systems - Christian-Albrechts-University Kiel
Dmitry Degtyar: Cognitive Energy Systems - Fraunhofer IEE
Dominik Köhler: Autonomous Energy Systems - Uni Kassel
Jan Viebahn: Data Scientist - TenneT TSO BV
Jean Thorey: Data Science and AI team member - RTE
Josephine Thomas: Machine Learning - University Greifswald
Karim Chaouache: Data Science and AI team member - RTE
Lukas Esterle: Distributed Computational Intelligence Group - Aarhus University
Manuel Wickert: Energy Informatics - Fraunhofer IEE
Marcel Arpogaus: Electrical engineering and information technology - HTWG Konstanz
Martin Braun: Energy Management and Power System Operation - University Kassel
Mira Jürgens: Department of Data analysis and mathematical modelling - Ghent University
Pawel Lytaev: Energy Management and Power System Operation - University Kassel
Pedro Vergara Barrios: Intelligent Electrical Power Grids - TU Delft
René Heindrich: Reinforcement Learning for Cognitive Energy Systems - University Kassel
Stavros Orfanoudakis: Intelligent Electrical Power Grids - TU Delft
Stefan Vogt: Algorithms AND Data Analysis Dev. Engineer - SMA Solar Technology AG
Sven Tomforde: Intelligent Systems - Christian-Albrechts-University Kiel
Pascal Plettenberg: Intelligent Embedded Systems - University Kassel
Mikhail Volkov: Laboratory of Chemoinformatics - University of Strasbourg
Franka Bause: Faculty of Computer Science - University of Vienna
Additional Programm Committee to be announced
Sponsors
Sponsors
The workshop Machine Learning for Sustainable Power Systems is sponsored by RTE, France's Transmission System Operator. We thank the sponsor for making this workshop possible.
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