Ramesh, Shyam Sundhar;
Sessa, Pier Giuseppe;
Krause, Andreas;
Bogunovic, Ilija;
(2022)
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems.
In: Koyejo, S and Mohamed, S and Agarwal, A and Belgrave, D and Cho, K and Oh, A, (eds.)
Proceedings of the 36th Conference on Neural Information Processing Systems: NeurIPS 2022.
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Abstract
Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information, with important applications, e.g., in wind energy systems. In this setting, the learner receives context (e.g., weather conditions) at each round, and has to choose an action (e.g., turbine parameters). Standard algorithms assume no cost for switching their decisions at every round. However, in many practical applications, there is a cost associated with such changes, which should be minimized. We introduce the episodic CBO with movement costs problem and, based on the online learning approach for metrical task systems of Coester and Lee [19], propose a novel randomized mirror descent algorithm that makes use of Gaussian Process confidence bounds. We compare its performance with the offline optimal sequence for each episode and provide rigorous regret guarantees. We further demonstrate our approach on the important real-world application of altitude optimization for Airborne Wind Energy Systems. In the presence of substantial movement costs, our algorithm consistently outperforms standard CBO algorithms.
| Type: | Proceedings paper |
|---|---|
| Title: | Movement Penalized Bayesian Optimization with Application to Wind Energy Systems |
| Event: | 36th Conference on Neural Information Processing Systems (NeurIPS) |
| Location: | ELECTR NETWORK |
| Dates: | 28 Nov 2022 - 9 Dec 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://openreview.net/forum?id=F-L7BxiE_V |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Artificial Intelligence, Computer Science, Computer Science, Computer Science, Information Systems, Science & Technology, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10198818 |
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