TY  - RPRT
AV  - public
N2  - 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.
TI  - GPU-accelerated stochastic predictive control of drinking water networks
EP  - 11
ID  - eprints3544
VL  - arXiv:1604.01074
PB  - arXiv
Y1  - 2016///
UR  - https://arxiv.org/abs/1604.01074
M1  - working_paper
A1  - Sampathirao, Ajay Kumar
A1  - Sopasakis, Pantelis
A1  - Bemporad, Alberto
A1  - Patrinos, Panagiotis
ER  -