Multireservoir modeling with dynamic programming and neural networks

For optimal multireservoir operation, a dynamic programming-based neural network model is developed in this study. In the suggested model, multireservoir operating rules are derived using a feed forward neural network from the results of three state variables' dynamic programming algorithm. The training of the neural network is done using a supervised learning approach with the back-propagation algorithm. A multireservoir system called the Parambikulam Aliyar project system is used for this study. The performance of the new multireservoir model is compared with (1) the regression-based approach used for deriving the multireservoir operating rules from optimization results; and (2) the single-reservoir dynamic programming–neural network model approach. The multireservoir model based on the dynamic programming–neural network algorithm gives improved performance in this study.