Logo eprints

GPU-accelerated stochastic predictive control of drinking water networks

Sampathirao, Ajay Kumar and Sopasakis, Pantelis and Bemporad, Alberto and Patrinos, Panagiotis GPU-accelerated stochastic predictive control of drinking water networks. Working Paper arXiv (Submitted)

WarningThere is a more recent version of this item available.
PDF - Submitted Version
Available under License Creative Commons Attribution Non-commercial.

Download (1MB) | Preview


Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona.

Item Type: Working Paper (Working Paper)
Identification Number: arXiv:1604.01074
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 04 Oct 2016 08:44
Last Modified: 04 Oct 2016 08:44
URI: http://eprints.imtlucca.it/id/eprint/3544

Available Versions of this Item

Actions (login required)

Edit Item Edit Item