Application of artificial neural network for optimal operation of a multi-purpose multi-reservoir system, I: initial solution and selection of input variables

In this two-part series research paper, the effect of different input variables for optimal operation of a multipurpose multi-reservoir system using artificial neural network (ANN) models has been thoroughly investigated. As an alternative to multi-reservoir stochastic model, dynamic programming-based ANN models are developed. Due to nonavailability of any specific criterion to select the number and type of input variables for such ANN model, an exhaustive study with various combinations of input variables for single-reservoir ANN models is done. Damodar Valley, a multi-purpose multi-reservoir system in India, is used for this study. Results obtained from discrete dynamic programming, an optimization technique, are used as training data for ANN models. With different combinations of input data, five types of ANN models are developed. ANN models are simulated with generated inflow sequence as well as different types of observed historical inflow sequences. For each simulation, 240 monthly networks are trained and stored. Four different stochastic dynamic programming (SDP) models of the system are developed considering one reservoir at a time approach. Linear regression analysis is performed between the results obtained from ANN models with results of the best SDP model. Objective function values as well as different regression parameters are compared to select preferred ANN model and its input variables.