Learning disaggregation technique for the operation of long-term hydroelectric power-systems

Learning disaggregation technique for the operation of long-term hydroelectric power-systems

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Article ID: iaor19991830
Country: United States
Volume: 30
Issue: 11
Start Page Number: 3195
End Page Number: 3202
Publication Date: Nov 1994
Journal: Water Resources Research
Authors: , , ,
Keywords: programming: dynamic, water, neural networks
Abstract:

This paper describes a non-linear disaggregation technique for the operation of multireservoir systems. The disaggregation is done by training a neural network to give, for an aggregated storage level, the storage level of each reservoir of the system. The training set is obtained by solving the deterministic operating problem of a large number of equally likely flow sequences. The training is achieved using the back propagation method, and the minimisation of the quadratic error is computed by a variable step gradient method. The aggregated storage level can be determined by stochastic dynamic programming in which all hydroelectric installations are aggregated to form one equivalent reservoir. The results of applying the learning disaggregation technique to Quebec's La Grande River are reported, and a comparison with the principal component analysis disaggregation technique is given.

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