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G-PST/ESIG Webinar Series: Reliable Energy Forecasting for Power System Operations
February 26 @ 4:00 pm - 5:00 pm EST
Featured Speakers: Akylas Stratigakos, Research Associate, Imperial College London; Wangkun Xu, Research Associate, Imperial College London
About the Webinar: Short-term energy forecasting, from a few minutes to a few days ahead, is critical for the near real-time operations of low-carbon power systems, enabling operators to better cope with variable and uncertain renewable production. Energy forecasting tools deployed in operational processes are increasingly based on machine learning methods, aggregating heterogeneous data from various sources, such as production measurements and weather forecasts. The resultant forecasts are subsequently used as input in decision-making tasks, such as market-clearing or scheduling processes. This talk will present novel ideas to improve the reliability of energy forecasts and operational decisions concerning two critical aspects: (i) dealing with missing data after model deployment, and (ii) tailoring energy forecasts to downstream decision-making tasks.
In the first part, we consider the problem of missing data after a model has been deployed in production, which could result from an equipment failure or cyberattack, and we will present novel forecasting methods that seamlessly handle missing data operationally by adapting to the available information. In the second part, we consider the issue of tailoring energy forecasts to the downstream decision tasks. Instead of solely focusing on statistical accuracy, we will present a smart predict-and-optimize (SPO) approach that embeds knowledge about the downstream decision task during model training, leading to more economical and robust decisions.
About the Speakers: Akylas Stratigakos received a Diploma in Electrical and Computer Engineering from the University of Patras, Greece, and a Ph.D. in Energy and Process Engineering from Mines Paris, PSL University, France. He is currently a Research Associate within the Department of Electrical and Electronic Engineering at Imperial College London. His research interests include energy forecasting, decision-making under uncertainty, and machine learning applications in power systems.
Dr. Wangkun Xu received a Ph.D. in Electrical Engineering from the Control and Power Research group at Imperial College London, in 2024. He is currently a research associate in the same group. His research interests include a broad range of machine learning, optimization, and control applications for secure and robust power system operations.
Moderator: Charlie Smith, Principal Consultant, ESIG
Registration Cost: FREE
Q&A Session: We will be using the slido platform for Q&A. Please submit your questions and follow-along during the event at this link.
