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

机译:供水系统中的混合能源系统评估:人工神经网络方法和方法

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摘要

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.
机译:供水系统是能源的主要消耗者,主要用于泵站和污水处理厂,因此,水务公司的首要任务是提高能源效率。当前的研究工作提出了一种基于可再生能源概念,液压系统性能,压力控制和神经网络的新方法论和计算算法,用于确定将在典型供水系统中应用的最佳混合能源配置。为了确定最佳的混合动力系统而创建的人工神经网络(ANN)使用的方案包括:仅电网供电,电网与水轮机结合,与风轮机结合以及与水电和风轮机的相互解决方案。根据从配置和经济型仿真器模型以及从液压和动力仿真器模型获得的值来训练ANN。获得的结果表明,该ANN高级计算模型可用于可持续混合能源系统计划中的决策支持解决方案,该计划可应用于供水系统或其他现有水电系统中,从而提高全球能源效率。

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