A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts

Publisher： John Wiley & Sons Inc

E-ISSN： 1944-7973|50|12|9267-9286

ISSN： 0043-1397

Source： WATER RESOURCES RESEARCH, Vol.50, Iss.12, 2014-12, pp. : 9267-9286

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

Related content

Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts

By  

WATER RESOURCES RESEARCH, Vol. 50, Iss. 10, 2014-10 ,pp. : 7847-7860

John Wiley & Sons Inc

Access to resources Recommend Favorite

Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm

By Li Gaoming  

Computational Geosciences, Vol. 15, Iss. 3, 2011-06 ,pp. : 451-462

Springer Publishing Company

Access to resources Recommend Favorite

Application of the two-stage Markov chain Monte Carlo method for characterization of fractured reservoirs using a surrogate flow model

By Ginting Victor  

Computational Geosciences, Vol. 15, Iss. 4, 2011-09 ,pp. : 691-707

Springer Publishing Company

Access to resources Recommend Favorite

A Hierarchical Bayesian Model Averaging Framework for Groundwater Prediction under Uncertainty

By  

GROUND WATER, Vol. 53, Iss. 2, 2015-03 ,pp. : 305-316

John Wiley & Sons Inc

Access to resources Recommend Favorite

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model

By  

WATER RESOURCES RESEARCH, Vol. 53, Iss. 11, 2017-11 ,pp. : 9444-9461

John Wiley & Sons Inc

Access to resources Recommend Favorite