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Education

• Ph.D in Applied Machine Learning for Energy Systems Management, Imperial College London, United Kingdom, 2020
• M.S. in Process Engineering, RWTH Aachen University, Germany, 2016
• B.S. in Electrical Engineering, RWTH Aachen University, Germany, 2016
• B.S. in Mechanical Engineering, RWTH Aachen University, Germany 2014

Work experience

• Since 2025: Associate Professor
  • Delft University of Technology, The Netherlands
  • Duties include: Research, education, valorization & leadership in AI for power system operation
• 2021-2024: Assistant Professor
  • Delft University of Technology, The Netherlands
  • Duties included: Research, education, valorization & leadership in AI for power system operation
• Since 2022: Principal Scientist
  • Austrian Institute of Technology, Center for Energy, Austria
  • Duties include: Leading a research programme with TU Delft
• Since 2021: Director (Non-Executive)
  • Student Energy, Canada
  • Duties include: Providing independent oversight (risk, strategy, performance, people)
• 2015-2016: Researcher
  • Massachusetts Institute of Technology, USA
• 2013-2014: Researcher
  • Carnegie Mellon University, USA

Awards

• Best Technical Brochure Award IEEE PES AMPS Subcommittee (2024)
• Outstanding Reviewer IEEE PES Transactions on Sustainable Energy (2024)
• Best Paper Award, IEEE Journal of Modern Power Systems and Clean Energy (2022)
• Eryl Cadwallader Davies Prize 2020-2021, Electrical Engineering, Imperial College (2021) for the top Outstanding PhD Thesis among 50 PhD theses in the academic year.
• John Lever Memorial Award (2019), Faculty of Engineering, Imperial College “contributed significantly to teaching, outreach, public engagement, enterprise or student experience.”
• John & Frances Jones Prize (2017-2018), Imperial College London for “the postgraduate student who ‘made the best all-round contribution to College life, taking into account academic achievement, social and extra-curricular activities, for the academic year in question”
• Best Paper Award, 6th International Conference on Smart Cities and Green ICT, Portugal (2017)
• Research Ambassador Award for highly qualified master’s students, RWTH Aachen (2015)
• The Germany Scholarship for academic achievements by Evonik Foundation, Bilfinger Berger (2012-2016)
• Dean’s List Award for academic achievements, Carnegie Mellon University, USA (2013-2014)
• International Study and Education Partnerships Grant to study in the USA by the German Academic Exchange Service (2013-2014)

Publications

Learning to Control a Battery Through Reinforcement: Balancing Lifetime and Profit

Santos Neves, C., Čović, N., & Cremer, J. (2025). 'Learning to Control a Battery Through Reinforcement: Balancing Lifetime and Profit.' Proceedings of IEEE PES PowerTech 2025, Kiel, Germany, 1–6.

Online Neural Dynamics Forecasting for Power System Security

Karaçelebi, M., & Cremer, J. L. (2025). 'Online Neural Dynamics Forecasting for Power System Security.' International Journal of Electrical Power & Energy Systems, 167, 110566.

Multi-Objective Reinforcement Learning for Power Grid Topology Control

Lautenbacher, T., Rajaei, A., Barbieri D., Viebahn, J., & Cremer, J. L. (2025). 'Multi-Objective Reinforcement Learning for Power Grid Topology Control.' Proceedings of IEEE PES PowerTech 2025, Kiel, Germany, 1–6.

Learning-Accelerated ADMM for Stochastic Power System Scheduling

Rajaei, A., Arowolo, O., & Cremer, J. L. (2025). 'Learning-Accelerated ADMM for Stochastic Power System Scheduling.' IEEE Transactions on Sustainable Energy.

Exploratory Analysis on the Impact of Grid Tariffs in Transmission Expansion Planning

Hofstadler, L., Gavriluta, C., Stiasny, J., & Cremer, J. (2025). 'Exploratory Analysis on the Impact of Grid Tariffs in Transmission Expansion Planning.' Proceedings of IEEE PES PowerTech 2025, Kiel, Germany, 1–6.

Predicting Power System Frequency with Neural Ordinary Differential Equations

Karaçelebi, M., & Cremer, J. L. (2025). 'Predicting Power System Frequency with Neural Ordinary Differential Equations.' Sustainable Energy, Grids and Networks (accepted).

Exploring Market Designs for Enhanced Flexibility Procurement with Deep Reinforcement Learning

Zobernig, V., Fanta, S., Strömer, S., Hemm, R., Cremer, J. L., & de Vries, L. J. (2025). 'Exploring Market Designs for Enhanced Flexibility Procurement with Deep Reinforcement Learning.' The International Conference on European Energy Markets, Lisboa, Portugal, 2025.

Exploring the Extrapolation Performance of Machine Learning Models for Power System Time Domain Simulations

Arowolo, O., Stiasny, J., & Cremer, J. L. (2025). 'Exploring the Extrapolation Performance of Machine Learning Models for Power System Time Domain Simulations.' Sustainable Energy, Grids and Networks (accepted).

Deep Diffusion on Sky Images for Probabilistic Ultra-Short-Term PV Forecasting

Lopez Romero, O., Poland, J., & Cremer, J. L. (2025). 'Deep Diffusion on Sky Images for Probabilistic Ultra-Short-Term PV Forecasting.' Proceedings of IEEE PES PowerTech 2025, Kiel, Germany, 1–6.

A Hybrid Curriculum Learning and Tree Search Approach for Network Topology Control

Meppelink, G. J., Rajaei, A., & Cremer, J. L. (2025). 'A Hybrid Curriculum Learning and Tree Search Approach for Network Topology Control.' Electric Power Systems Research, 242, 111455.

The impact of the growing use of machine learning/artificial intelligence in the operation and control of power networks from an operational perspective

Mouadh Yagoubi, Sjoerd P.J. Kop, Jochen Cremer, Marija Ilic, Amarsagar Reddy Ramapuram, Ming Dong, Karin Rodrigues, Guangchao Geng, Milos Subasic, Adrian Kelly, Alberto Kopiler, Rohit Anand, Teerasak Arunthanakij, Victor Meza, Fabian Heymann, Wolf Berwouts, Jingyu Wang, Medha Subramanian, Panagiotis Papadopoulos, Koen Vandermot, Samuel Young, Rohit Trivedi, Spyros Chatzivasileiadis, Viktor Eriksson Möllerstedt, Arnaud Zinflou., 'The impact of the growing use of machine learning/artificial intelligence in the operation and control of power networks from an operational perspective', CIGRE TB 946, WG C2.42.

Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems

Dong, J., Xie, H., Liao, Y., Cremer, J. L., & Esfahani, P. M. (2024). 'Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems.' IEEE Transactions on Control Systems.

Polynomial Line Outage Distribution Factors for Estimating Expected Congestion and Security

Cremer, J. L. (2024). 'Polynomial Line Outage Distribution Factors for Estimating Expected Congestion and Security.' IEEE Transactions on Power Systems.

Non-stationarity in multiagent reinforcement learning in electricity market simulation

Renshaw-Whitman, C., Zobernig, V., Cremer, J. L., & de Vries, L. (2024). 'Non-stationarity in multiagent reinforcement learning in electricity market simulation.' Electric Power System Research, 235, 110712.

A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks

Cremer, J. L., Kelly, A., Bessa, R. J., Subasic, M., & Papadopoulos, P. (2024). 'A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks.' IEEE ISGT Europe 2024.

Learning a reward function for user-preferred appliance scheduling

Covic, N., Cremer, J. L., & Pandžić, H. (2024). 'Learning a reward function for user-preferred appliance scheduling.' Electric Power System Research, 235, 110667.

Few-Shot Transfer Learning for Battery Cycle Life

Yu, R., Wang, J., Han, Y., O’Connor, T. S., & Cremer, J. L. (2024). 'Few-Shot Transfer Learning for Battery Cycle Life.' IEEE ISGT Europe 2024

Constraint-driven deep learning for N-k security constrained optimal power flow

Giraud, B., Rajaei, A., & Cremer, J. L. (2024). 'Constraint-Driven Deep Learning for N-k Security Constrained Optimal Power Flow.' Electric Power System Research, 235, 110692.

Tensor Convolution-Based Aggregated Flexibility Estimation in Active Distribution Systems

Chrysostomou, D., Torres, J. R., & Cremer, J. L. (2024). 'Tensor Convolution-Based Aggregated Flexibility Estimation in Active Distribution Systems.' IEEE Transactions on Smart Grid.

Automatic Detection of Subsynchronous Oscillations

Neagu, A., Chakravorty, D., & Cremer, J. L. (2024). 'Automatic Detection of Subsynchronous Oscillations.' CIGRE Annual Meeting 2024, C4-11096-2024.

Spatio-Temporal Data-Driven and Machine Learning-Based Applications for Transmission Systems

Sevilla, F. R. S., Liu, Y., Barocio, E., Korba, P., & Cremer, J. L. (2024). 'Spatio-Temporal Data-Driven and Machine Learning-Based Applications for Transmission Systems.' IEEE PES General Meeting 2024.

Deep Statistical Solver for Distribution System State Estimation

Habib, B., Isufi, E., Breda, W. v., Jongepier, A., & Cremer, J. L. (2024). 'Deep Statistical Solver for Distribution System State Estimation.' IEEE Transactions on Power Systems, 39(2), 4039-4050.

Game-Theoretic Learning for Power System Dynamic Ancillary Service Provisions

Xie, H., & Cremer, J. L. (2024). 'Game-Theoretic Learning for Power System Dynamic Ancillary Service Provisions.' IEEE Control Systems Letters, 8, 1307-1312.

Generating Quality Datasets for Real-Time Security Assessment: Balancing Historically Relevant and Rare Feasible Operating Conditions

Bugaje, A.-A., Cremer, J. L., & Strbac, G. (2023). 'Generating Quality Datasets for Real-Time Security Assessment: Balancing Historically Relevant and Rare Feasible Operating Conditions.' International Journal of Electrical Power & Energy Systems, 154, 109427.

Dynamic Incremental Learning for real-time disturbance event classification

Veera Kumar, N., Cremer, J. L., & Popov, M. (2023). 'Dynamic Incremental Learning for real-time disturbance event classification.' International Journal of Electrical Power & Energy Systems, 148, 108988.

Machine learning and digital twins: monitoring and control for dynamic security in power systems

Brosinsky, Christoph, Mert Karaçelebi, and Jochen L. Cremer. 'Machine learning and digital twins: monitoring and control for dynamic security in power systems.' In Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques, pp. 79-106. Elsevier, 2023.

More than accuracy: end-to-end wind power forecasting that optimises the energy system

Wahdany, D., Schmitt, C., & Cremer, J. L. (2023). 'More than accuracy: end-to-end wind power forecasting that optimises the energy system.' Electric Power System Research, 221, 109384.

Transient Stable Corrective Control Using Neural Lyapunov Learning

Bellizio, F., Cremer, J. L., & Strbac, G. (2022). 'Transient Stable Corrective Control in Smart Grids Using Neural Lyapunov Learning.' IEEE Transactions on Power Systems, 38, 3245-3253.

Regularized Learning with Physical Equations for Uncertain Power System Dynamics

Xie, H., Bellizio, F., & Cremer, J. L. (2023). 'Regularized Learning with Physical Equations for Uncertain Power System Dynamics.' IEEE PowerTech 2023, Belgrade, Serbia, 1-8.

Exploring Operational Flexibility of Active Distribution Networks with Low Observability

Chrysostomou, D., Torres, J. R., & Cremer, J. L. (2023). 'Exploring Operational Flexibility of Active Distribution Networks with Low Observability.' IEEE PowerTech 2023, Belgrade, Serbia, 1-8.

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Vohra, R., Rajaei, A., & Cremer, J. L. (2023). 'End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting.' IEEE PowerTech 2023, Belgrade, Serbia, 1-8.

MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-Based Residential Demand Response

van Tilburg, J., Siebert, L. C., & Cremer, J. L. (2023). 'MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-Based Residential Demand Response.' IEEE PowerTech 2023, Belgrade, Serbia, 1-8.

Split-based sequential sampling for realtime security assessment

Bugaje, A.-A., Cremer, J. L., & Strbac, G. (2023). 'Split-based sequential sampling for realtime security assessment.' International Journal of Electrical Power & Energy Systems, 146, 108790.

Real-Time Transmission Switching with Neural Networks

Bugaje, A.-A., Cremer, J. L., & Strbac, G. (2023). 'Real-Time Transmission Switching with Neural Networks.' IET Generation, Transmission & Distribution, 17(3), 696-705.

Application of spatio-temporal data-driven and machine learning algorithms for security assessment taskforce”, by IEEE AMPS taskforce on Application of Big Data Analytics on Transmission Systems for Dynamic Security Assessment

Rafael Segundo, Yanli Liu, Emilio Barocio, Petr Korba, Aharon de la Torre, Al-Amin Bugaje, Alejandro Zamora-Mendez, Alexandra Karpilow, Carlos Toledo, Claudia Caro-Ruiz, Daniel Dotta, Daniel Müller, David Panchi, Diego Echeverría, Federica Bellizio, Francisco Zelaya, Gabriel V. de S. Lopes, Gao Qiu, Garibaldi Pineda-Garcia, Goran Strbac, Hector Chavez, Hjörtur Jóhannsson, Jaime Cepeda, Jochen L. Cremer, et.al., 'Application of spatio-temporal data-driven and machine learning algorithms for security assessment', IEEE PES Technical Brochure IEEE PES-TR104, (2022).

Learning to Run a Power Network with Trust

Marot, A., Donnot, B., Chaouache, K., Kelly, A., & Cremer, J. L. (2022). 'Learning to Run a Power Network with Trust.' Electric Power Systems Research, 212, 108487.

Present situation on data acquisition, handling, and analytics of operators of the transmission system in different countries and their future needs to cope with the continuous growth of data

Marcos Netto, Venkat Krishnan, Michael Ingram, Yajun Wang, Junbo Zhao, Emilio Barocio, Manuel Andrade, Juan Jose Guerrero, Jorge Mola, Jaime C. Cepeda, David Panchi, Aharon B. De La Torre, Diego E. Echeverría, Hector Chavez, Alberto Trigueros, Miguel Herrera, Jose Luis Rueda Torres, Simon Tindemans, Marnick Huijsman, Jorrit Bos, Danny Klaar, Petr Korba, Mats Larsson, Mario Paolone, Miguel Ramirez, Rusejla Sadikovic, Walter Sattinger, Rafael Segundo, Camille Hamon, Robert Eriksson, Jochen Cremer, et.al. 'Present situation on data acquisition, handling, and analytics of operators of the transmission system in different countries and their future needs to cope with the continuous growth of data', IEEE PES Technical Brochure IEEE PES-TR100, (2022).

Perspectives on Future Power System Control Centers for Energy Transition

Marot, A., Kelly, A., Naglic, M., Barbesant, V., Cremer, J. L., & Stefanov, A. (2022). 'Perspectives on Future Power System Control Centers for Energy Transition.' IEEE Journal of Modern Power Systems and Clean Energy, 10, 328-344.

Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market

Bellizio, F., Zu, W., Qiu, D., Ye, Y., & Cremer, J. L. (2022). 'Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market, 111(7), 1234-1245.

Verifying Machine Learning Conclusions for Securing Low Inertia Systems

Bellizio, F., Bugaje, A.-A., Cremer, J. L., & Strbac, G. (2022). 'Verifying Machine Learning Conclusions for Securing Low Inertia Systems.' Sustainable Energy, Grids, and Networks, 30, 100656.

State-of-the-Art of Data Collection, Analytics, and Future Needs of Transmission Utilities Worldwide

Sevilla, F. R. S., Liu, Y., Barocio, E., Korba, P., & Cremer, J. L. (2022). 'State-of-the-Art of Data Collection, Analytics, and Future Needs of Transmission Utilities Worldwide.' International Journal of Electrical Power & Energy Systems, 137, 107772.

Machine-Learned Security Assessment for Changing System Topologies

Bellizio, F., Cremer, J. L., & Strbac, G. (2022). 'Machine-Learned Security Assessment for Changing System Topologies.' International Journal of Electrical Power & Energy Systems, 134, 107380.

Selecting decision trees for power system security assessment

Bugaje, A., Cremer, J. L., Sun, M., & Strbac, G. (2021). 'Selecting decision trees for power system security assessment.' Energy and AI, 6, 100110.

A Causality-Based Feature Selection Approach for Data-Driven Dynamic Security Assessment

Bellizio, F., Cremer, J. L., & Strbac, G. (2021). 'A Causality-Based Feature Selection Approach for Data-Driven Dynamic Security Assessment.' Electric Power Systems Research, 201, 107537.

A Confidence-Aware Machine-Learned Framework for Dynamic Security Assessment

Zhang, T., Sun, M., Cremer, J. L., & Strbac, G. (2021). 'A Confidence-Aware Machine-Learned Framework for Dynamic Security Assessment.' IEEE Transactions on Power Systems, 36 (5), 1234-1245.

Operating strategies and preparedness for system operational resilience

CIGRE TB 833, 'Operating strategies and preparedness for system operational resilience', CIGRE WG C2.25.

From Optimization-Based Machine Learning to Interpretable Security Rules for Operation

Cremer, J. L., Konstantelos, I., & Strbac, G. (2019). 'From Optimization-Based Machine Learning to Interpretable Security Rules for Operation.' IEEE Transactions on Power Systems, 34 (5), 5678-5690.

Optimized Operation Rules for Imbalanced Classes

Cremer, J. L., Konstantelos, I., & Strbac, G. (2019). 'Optimized Operation Rules for Imbalanced Classes.' IEEE PES General Meeting of Power Systems.

Data-Driven Power System Operation: Exploring the Balance Between Cost and Risk

Cremer, J. L., Konstantelos, I., Tindemans, S. H., & Strbac, G. (2019). 'Data-Driven Power System Operation: Exploring the Balance Between Cost and Risk.' IEEE Transactions on Power Systems, 34 (1), 791-801.

Day-Ahead Scheduling of Electric Heat Pumps for Peak Shaving in Distribution Grids

Pau, M., Cremer, J. L., Ponci, F., & Monti, A. (2018). 'Day-Ahead Scheduling of Electric Heat Pumps for Peak Shaving in Distribution Grids.' Communications in Computer and Information Science, 921, 27–51.

A Novel Data-Driven Scenario Generation Framework for Transmission Expansion Planning with High Renewable Energy Penetration

Sun, M., Cremer, J. L., & Strbac, G. (2018). 'A Novel Data-Driven Scenario Generation Framework for Transmission Expansion Planning with High Renewable Energy Penetration.' Applied Energy, 228, 546-555.

Sample-Derived Disjunctive Rules for Secure Power System Operation

Cremer, J. L., Konstantelos, I., Tindemans, S. H., & Strbac, G. (2018). 'Sample-Derived Disjunctive Rules for Secure Power System Operation.' IEEE International Conference on Probabilistic Methods Applied to Power Systems, Boise, USA, 1-6.

Impact of Customers Flexibility in Heat Pumps Scheduling for Demand Side Management

Pau, M., Cremer, J. L., Ponci, F., & Monti, A. (2017). 'Impact of Customers Flexibility in Heat Pumps Scheduling for Demand Side Management.' IEEE International Conference on Environment and Electrical Engineering, 1-6.

Optimal Scheduling of Heat Pumps for Power Peak Shaving and Customers’ Thermal Comfort

Cremer, J. L., Pau, M., Ponci, F., & Monti, A. (2017). 'Optimal Scheduling of Heat Pumps for Power Peak Shaving and Customers’ Thermal Comfort.' International Conference on Smart Cities and Green ICT Systems, Portugal, 23-34.

Risk-Based Integrated Production Scheduling and Electricity Procurement for Continuous Power-Intensive Processes

Zhang, Q., Cremer, J. L., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). 'Risk-Based Integrated Production Scheduling and Electricity Procurement for Continuous Power-Intensive Processes.' Computers & Chemical Engineering, 86, 90-105.

Electric Energy Procurement for Large Industrial Consumers Under Uncertainty in Electricity Price and Product Demand

Zhang, Q., Cremer, J. L., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2014). 'Electric Energy Procurement for Large Industrial Consumers Under Uncertainty in Electricity Price and Product Demand.' AIChE Annual Meeting, USA, 366568.

Talks

Tutorial at ACM e-Energy 2025

June 17, 2025

Talk at ACM, Rotterdam, the Netherlands

Tutorial at DTU Summer School 2025

May 18, 2025

Talk at DTU Summer School, Copenhagen, Denmark

Seminar Liège: Deep Learning for Power System Reliability Assessments

May 09, 2025

Talk at University of Liège, Liege, Belgium

All AI models are wrong, but some are useful… for power systems

April 08, 2025

Talk at ENTSO-e, Brussel, Belgium

1-Day Course on AI in Power System Reliability Monitoring

January 21, 2025

Tutorial at Tallinn University, Tallinn, Estonia

Tutorial CIGRE C2.42 at AI-Day for Grid Operations

November 18, 2024

Tutorial at IEEE CIGRE C2.42 AI-Day, Paris, France

Energietage 2024

November 15, 2024

Panel at Energietage 2024, Vienna, Austria

ML/AI Applications in Power Systems: Drivers and Barriers

October 14, 2024

Panel at IEEE ISGT Europe 2024, Dubrovnik, Croatia

IEEE PES & Power Africa Conference 2024

October 10, 2024

Tutorial at IEEE PES & IAS PowerAfrica Conference 2024, Johannesburg, South Africa

Inverter-Based Power Systems Summer School

September 02, 2024

Tutorial at Imperial College London, London, UK

AI for NetZero Webinar Series

September 01, 2024

Talk at AI for NetZero Webinar Series, Online

Convergence of AI- and Physics-Based Approaches in Power System Analysis, Optimization, and Control

July 22, 2024

Panel at IEEE PES General Meeting 2024, Seattle, USA

Dynamic Modeling of Multi-Energy Systems

July 21, 2024

Panel at IEEE PES General Meeting 2024, Seattle, USA

Reinforcement Learning for Energy Community Management

June 17, 2024

Talk at Workshop on Reinforcement Learning for Stochastic Networks (RL4SN), Toulouse, France

Webinar at IEEE PES Chapter Spain, Women in Power

May 31, 2024

Talk at Universitat Politècnica de Catalunya, Barcelona, Spain

Rise of AI: Opportunity or Threat?

May 28, 2024

Tutorial at Dutchpower Symposium, Amsterdam, Netherlands

Advancing Power System Reliability with System-based Deep Learning

May 27, 2024

Talk at Electrimacs Conference 2024, Castellón, Spain

Constraint-Driven Deep Learning for N-k Security Constrained Optimal Power Flow

March 12, 2024

Talk at EPSRC Supergen Energy Networks Hub Risk and Resilience Day, Newcastle University, UK

Weakly-supervised Graph Neural Networks for Distribution System State Estimation

February 04, 2024

Talk at 3rd Champéry Power Conference, Champéry, Switzerland

33rd Workshop on Computational Intelligence

November 23, 2023

Keynote at HTW Berlin, Berlin, Germany

AI-based Monitoring for Sustainable Energy Systems

November 09, 2023

Keynote at Stichting Pipeliner Symposium '23, Rotterdam, Netherlands

Weakly Supervised Learning for Power Grid State Estimation

August 20, 2023

Talk at 10th International Congress on Industrial and Applied Mathematics (ICIAM 2023), Tokyo, Japan

Graph Neural Networks for Distribution State Estimation

July 20, 2023

Panel at IEEE PES General Meeting 2023, Orlando, USA

Machine Learning to Prevent Blackouts in Power Systems

July 19, 2023

Panel at IEEE PES General Meeting 2023, Orlando, USA

Young Professionals Panel Session on Future Power System Workforce

June 29, 2023

Panel at IEEE PES PowerTech 2023, Belgrade, Serbia

Graph Neural Networks for Distribution State Estimation

June 27, 2023

Panel at IEEE PES PowerTech 2023, Belgrade, Serbia

AI and ML for Smart Grids

March 20, 2023

Round Table Discussion at Smart Grid Tech Week, Amsterdam, Netherlands

AI for Energy – Exploring Activities and Opportunities in the Netherlands

February 13, 2023

Keynote at DOE Workshop on AI for Energy Innovation, Washington, D.C., USA

Artificial Intelligence for Active Power Distribution

October 11, 2022

Panel at IEEE ISGT Europe 2022, Novi Sad, Serbia

Stability, Dynamics, and Control of Emerging Renewable-Rich Power Systems

October 10, 2022

Panel at IEEE ISGT Europe 2022, Novi Sad, Serbia

AI for Distributed Energy Systems

September 01, 2022

Keynote at Intelligent Systems Conference (IntelliSys) 2022, Amsterdam, Netherlands

Machine Learning for Security Assessments in Low Inertia Grids

July 20, 2022

Panel at IEEE PES General Meeting 2022, Denver, USA

Multi-Agent Reinforcement Learning for Incentivized Flexibility from Residents

July 19, 2022

Panel at IEEE PES General Meeting 2022, Denver, USA

Learning to Run a Power Network with Trust

July 19, 2022

Panel at IEEE PES General Meeting 2022, Denver, USA

AI Methods For Realtime Dynamic Security Assessment

May 25, 2022

Panel at IEEE International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA) 2022, Split, Croatia

On Dynamics and Artificial Intelligence for Power Systems

May 24, 2022

Panel at IEEE International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA) 2022, Split, Croatia

AI Innovations in Energy Systems

October 13, 2021

Talk at World Summit AI 2021, Amsterdam, Netherlands

AI: Real-Time Operations or Full Automation?

September 15, 2021

Keynote at ISA Knowledge Days 2021, Virtual Event

CIGRE C2.25 Operational Resilience

August 20, 2021

Tutorial at CIGRE Annual Meeting 2021, Online, Paris

Advanced Data Analytics in Power Systems

December 15, 2019

Talk at IEEE Big Data & Analytics Webinar 2019, Online

Machine Learning Based Operation of Dynamic Sustainable Energy Systems

November 21, 2019

Panel at IEEE Sustainable Power and Energy Conference 2019, Beijing, China

A Student Perspective on Climate Change

October 10, 2019

Keynote at AAPG Energy Transition Forum 2019, Edinburgh, UK

Data Analytic Tools for Security Assessment of Bulk Power Systems

August 05, 2019

Panel at IEEE PES General Meeting 2019, Atlanta, USA

Teaching

SET3125: Machine Learning Workflows for Digital Energy Systems

EE4C12: Machine Learning for Electrical Engineering

EEX01: Introduction to Machine Learning

Editorial and reviewer service

• Associate Editor, IEEE PES Transactions on Power Systems, 2025-2027
• Technical program committee, 24th Power Systems Computation Conference (PSCC), Cyprus, 2026
• Technical program committee, 16th ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), Rotterdam, 2025
• Guest editor for a special issue on “VSI:Stability Analysis of transmission system” International Journal of Electrical Power Engineering, 2025
• Technical program committee 12th DACH+ Energy Informatics Conference, Lugano, Switzerland, 2024
• Technical program committee 15th ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), Singapore, 2024
• Guest editor for a special issue on “Big Data Analytics: Moving towards applications to maintain the secure operation of carbon-neutral power grids” IEEE Open Access Journal of Power and Energy, 2024
• Guest editor for a special issue on “Explainable AI for Smart Grids” for IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2023
• Technical program committee IEEE ISGT Europe, Novi Sad, Serbia, 2022
• Guest editor for a special issue on “Applications of machine learning and statistical methods for dynamic security assessment on transmission systems”, Sustainable Energy, Grids and Networks, 2018
• Reviewer of around 100+ IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, IEEE Transactions on Energy Markets, Policy, and Regulation, International Journal of Electrical Power & Energy Systems, Electrical Power Systems Research

Service and leadership

• Co-Organizer, 14th (Hybrid) Symposium on Communications for Energy Systems (COMFOREN), Vienna, 09/2024
• Organiser workshop for industry, “AI use cases for distribution networks”, Alliander, 05/2024
• Moderator Symposium “Wide area monitoring, and data-driven aspects for the future power system” Inauguration of Prof. Marjan Popov, Delft, 03/2024
• Moderator panel “Roadmap of Innovating with AI for Distribution Grids”, IEEE PES ISGT Europe 2023, Grenoble France
• Chair and organiser 7th International DynPower Workshop, TU Delft, 09/2023
• Co-Chair technical session “Monitoring and control”, IEEE PES PowerTech Belgrade, 06/2023
• Organiser workshop “GNNs and RL for Energy System Planning and Operation”, Austrian Institute of Technology, Vienna, Austria, 03/2023
• Panel chair “Data analytics, machine learning, and artificial intelligence in smart grids”, IEEE European Conference on Innovative Smart Grid Technologies, Novi Sad, Serbia, 10/2022
• Panel chair “Advanced Applications of Modern Optimization and Artificial Intelligence Methods on Active Distribution Networks”, IEEE PES General Meeting, Denver, USA, 08/2022
• Panel chair “Data collection and future needs to account for the continuous growth of sensing data in control rooms”, IEEE PES General Meeting, Denver, USA, 08/2022
• Organizer, AI for E&S Think Tank Seminar Series, (bi-)weekly TU Delft, Rotterdam, Leiden University, 04/2021
• Panel moderator “Action-orientated innovations from the private sector” World Summit AI, Amsterdam, 10/2021
• Workshop, “Entrepreneurship, Climate Change, and Impact”, University of Leeds, UK, 12/2019
• Founder, Climate Entrepreneurs Club London
• Chair and moderator of the 3-day conference, International Student Energy Summit 2019, London, UK 700 attendees, 07/2019