Hybrid energy system evaluation in water supply systems: artificial neural network approach and methodology

Abstract

Water supply systems are large consumers of energy mainly used in pumping stations and treatment plants, a major priority for water utilities being, therefore, the improvement of energy efficiency. The current research work presents a new methodology and a computational algorithm based on renewable energy concepts, hydraulic system behaviour, pressure control and neural networks for the determination of the best hybrid energy configuration to be applied in a typical water supply system. The artificial neural network (ANN) created to determine the best hybrid system uses scenarios with grid only supply, grid combined with hydro turbine, with wind turbine and mutual solution with hydro and wind turbine. The ANN is trained based on values obtained from a configuration and economical simulator model, as well as from a hydraulic and power simulator model. The results obtained show this ANN advanced computational model is useful for decision support solutions in the plan of sustainable hybrid energy systems that can be applied in water supply systems or others existent hydro systems allowing the improvement of the global energy efficiency.