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Machine learning based approaches for decision & control under uncertainties (in electric power systems)

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If you have a question about this talk, please contact Dr Guy-Bart Stan .

The talk will present two approaches for designing sequential decision strategies by exploiting ideas from supervised learning. The first one is a non-model based batch-mode reinforcement learning algorithm iteratively fitting Q-functions to a random sample of four-tuples (state, action, reward, next-state). The second one is a model based multi-stage stochastic programming algorithm extracting and aggregating optimized decisions computed over an ensemble of incomplete disturbance trees. The algorithms are motivated by and illustrated on electric power systems planning and control problems.

This talk is part of the CUED Control Group Seminars series.

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Live Zoom webinar, followed by Q&A session William Harvey Lecture Theatre, Clinical School, Addenbrooke's Institute of Metabolic Science (IMS), Level 4 ATC, Seminar rooms 1,2 & 3

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