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A Neuro-dynamic Programming Approach for Stochastic Reservoir Management

Bernard LamondAbdeslem BoukhtoutaUniversité Laval. Faculté des sciences de l'administration. Direction de la recherche

出版	Faculté des sciences de l'administration de l'Université Laval, Direction de la recherche, 2002
ISBN	28952415039782895241508
URL	http://books.google.com.hk/books?id=s1GZAAAACAAJ&hl=&source=gbs_api

註釋

We propose an approach based on neural networks for optimizating a single hydroelectric reservoir. A stochastic neuro-dynamic programming algorithm is used to approximate the future value function by a neural function. The latter is used in deriving the optimal policy. The approximation architecture, based on the feedforward network, gives very smooth approximate functions even with a coarse discretization of the state and action variables. The hydroelectric reservoir model presented in our study assumes a piecewise linear reward of the electricity produced and takes into account the turbine head effects and the stochastic inflows. The method is illustrated with a numerical example.