On-Line Design of Water Reservoir Policies Based on Inflow Prediction. Castelletti, A., de Rigo, D., Tepsich, L., Soncini-Sessa, R., & Weber, E. 41(2):14540–14545. ![On-Line Design of Water Reservoir Policies Based on Inflow Prediction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Stochastic Dynamic Programming (SDP) is the method most extensively adopted to design release policies for water reservoir networks. However, it suffers of the well known "curse of dimensionality", which actually limits its applicability to small reservoir networks. In this paper we present an on-line approach to policy design that not only constitutes a viable alternative to overcome the SDP limits, but can also be used with an inflow predictor to improve the performance of SDPbased off-line policies. This latter possibility is explored and discussed through a real world case study.
@article{castellettiOnlineDesignWater2008,
title = {On-Line Design of Water Reservoir Policies Based on Inflow Prediction},
author = {Castelletti, Andrea and de Rigo, Daniele and Tepsich, Luca and Soncini-Sessa, Rodolfo and Weber, Enrico},
editor = {Myung, Chung and Misra, Pradeep},
date = {2008-07},
journaltitle = {IFAC-PapersOnLine},
volume = {41},
pages = {14540--14545},
issn = {1474-6670},
doi = {10.3182/20080706-5-kr-1001.02463},
url = {https://doi.org/10.3182/20080706-5-KR-1001.02463},
abstract = {Stochastic Dynamic Programming (SDP) is the method most extensively adopted to design release policies for water reservoir networks. However, it suffers of the well known "curse of dimensionality", which actually limits its applicability to small reservoir networks. In this paper we present an on-line approach to policy design that not only constitutes a viable alternative to overcome the SDP limits, but can also be used with an inflow predictor to improve the performance of SDPbased off-line policies. This latter possibility is explored and discussed through a real world case study.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-11681083,dddas,dynamic-data-driven-application-system,featured-publication,integrated-water-resources-management,integration-techniques,optimisation,partial-open-loop-feedback-control,polfc,reservoir-management,stochastic-dynamic-programming,water-reservoir-management,water-resources},
number = {2},
options = {useprefix=true}
}
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