Comparative analysis of evolving artificial neural network and reinforcement learning in stochastic optimization of multireservoir systems

Abstract

Dynamic programming (DP) has been among the most popular techniques for solving multireservoir problems since the early 1960s. However, DP and DP-based methods suffer from two serious issues: namely, the curses of modelling and dimensionality. Later, reinforcement learning (RL) was introduced to overcome some deficiencies in the traditional DP mainly related to the curse of modelling, but it still encounters the curse of dimensionality in larger systems. Recently, the artificial neural network has emerged as an effective approach to solve stochastic optimization of reservoir systems with high flexibility. In this paper, we develop a single-step evolving artificial neural network (SENN) model that overcomes the curses of modelling and dimensionality in a multireservoir system. Furthermore, a novel efficient allocation technique is developed to ease the allocation of water among different users. A two-reservoir system in Karkheh Basin, Iran, is applied to derive and test the methods. Elitist-mutated particle swarm optimization is used to train the network. A comparison of the results with Q-learning shows the superiority of the SENN, especially during drought periods. Moreover, the SENN performs better in producing more hydropower energy in the system. Thus, the main contributions of this research are (1) development of SENN applications to multireservoir systems, (2) a comparative analysis between SENN and Q-learning especially in prolonged drought conditions and (3) a proposed efficient optimal allocation technique using the simulation method.