An Integrated Approach to Optimizing the Energy Efficiency of Water Supply—The Way to Achieve Effective Management

Abstract

Escalating energy costs in recent years have spurred an examination of energy cost reduction strategies. Among these, exploring spot markets and building photovoltaic power plants at water facilities have emerged as promising options. This study focuses on the utilization of genetic algorithms to optimize pumping operations. Through simulations and case studies on small and medium-sized water distribution networks in the Czech Republic, the effectiveness of genetic algorithms at reducing operational costs is demonstrated. The integration of neural networks for predictive modeling and real-time decision-making complements the genetic algorithm approach, promising significant operational savings amid evolving energy market dynamics.