Efficient River Management using Stochastic MPC and Ensemble Forecast of Uncertain In-flows ⁎ ⁎The first and the third authors acknowledge the financial support from the Australian Research Council Linkage Project (LP130100605) and the Brescia Smart Living Project (MIURSCN00416) respectively.
Author(s) -
Hasan Arshad Nasir,
Tony Zhao,
Algo Carè,
Quan J. Wang,
Erik Weyer
Publication year - 2018
Publication title -
ifac-papersonline
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 72
eISSN - 2405-8971
pISSN - 2405-8963
DOI - 10.1016/j.ifacol.2018.06.196
Subject(s) - linkage (software) , drainage basin , streamflow , resource (disambiguation) , control (management) , computer science , ensemble forecasting , water resources , operations research , environmental science , environmental resource management , geography , mathematics , ecology , biology , machine learning , artificial intelligence , gene , biochemistry , cartography , chemistry , computer network
Efficient river management is essential in improving water resource utilisation. However, river flows and water-levels are affected by unregulated in- and out-flows. Therefore, it is important to consider the forecasts of these unregulated flows and the uncertainties in the forecasts. The paper describes control and modelling tools from the literature that suit the river management problem. Specifically, a scenario-based Stochastic Model Predictive Control (MPC) strategy, that makes use of ensemble forecast of unregulated flows, is proposed, where the ensemble forecast contains multiple flow scenarios to characterise future flows and their uncertainties, and are obtained from catchment hydrological models.
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