Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Abstract

AbstractIn this article, smart water‐energy management based on an artificial neural network for a photovoltaic‐wind system coupled with water pumping and reverse osmosis desalination unit with hydraulic storage is developed. The proposed system was designed to meet freshwater demand for remote areas of Tunisia's south. The authors focus on an artificial neural network‐type management strategy, ensuring power‐sharing between sources, hydraulic components, and tank storage. The main objective of the management strategy is to deliver and purify as much freshwater as possible by taking advantage of all the energy available according to climatic conditions. A parametric sensitivity algorithm is developed to find the most appropriate neural network architecture. A dynamic simulator for 1 year of operation integrates the energy management of the system is developed. The used water‐energy management‐based artificial neural network showed good results of power‐sharing during a fast time with a correlation coefficient and a coefficient of determination between the actual and estimated values of the three motor pumps electrical power equal to 96% and 98%, respectively. Also, the root mean squared error was acceptable at 0.1476. These results have been proved over a year and can be used by other researchers with a similar system.