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Learning Optimal Control of Water Distribution Networks Through Sequential Model-Based Optimization

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Learning and Intelligent Optimization (LION 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12096))

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Abstract

Sequential Model-based Bayesian Optimization has been successfully applied to several application domains, characterized by complex search spaces, such as Automated Machine Learning and Neural Architecture Search. This paper focuses on optimal control problems, proposing a Sequential Model-based Bayesian Optimization framework to learn optimal control strategies. The strategies are synthetized by pressure-based rules, whose parameters are the design variables of the optimization problem whose black-box objective is the energy cost. A Bayesian optimization framework is presented which handles a quite general formalization of the control problem including multiple constraints, also black box. Relevant results on a real-life Water Distribution Network are reported, comparing different possible choices for the proposed framework.

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Acknowledgements

This study has been partially supported by the Italian project “PerFORM WATER 2030” – programme POR (Programma Operativo Regionale) FESR (Fondo Europeo di Sviluppo Regionale) 2014–2020, innovation call “Accordi per la Ricerca e l’Innovazione” (“Agreements for Research and Innovation”) of Regione Lombardia, (DGR N. 5245/2016 - AZIONE I.1.B.1.3 – ASSE I POR FESR 2014–2020) – CUP E46D17000120009.

We greatly acknowledge the DEMS (Department of Economics, Management and Statistics) Data Science Lab for supporting this work by providing computational resources.

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Authors and Affiliations

  1. Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126, Milan, Italy

    Antonio Candelieri

  2. Department of Computer Science Systems and Communication, University of Milano-Bicocca, 20126, Milan, Italy

    Bruno Galuzzi, Ilaria Giordani & Francesco Archetti

Authors
  1. Antonio Candelieri
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  2. Bruno Galuzzi
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  3. Ilaria Giordani
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  4. Francesco Archetti
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Corresponding author

Correspondence to Antonio Candelieri .

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Editors and Affiliations

  1. CARGO Lab., Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias S. Kotsireas

  2. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Candelieri, A., Galuzzi, B., Giordani, I., Archetti, F. (2020). Learning Optimal Control of Water Distribution Networks Through Sequential Model-Based Optimization. In: Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2020. Lecture Notes in Computer Science(), vol 12096. Springer, Cham. https://doi.org/10.1007/978-3-030-53552-0_28

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