Energetic optimization in irrigation systems: Layout generation with energy criteria using graph and clustering techniques

The growth of the global population and economic development have resulted in a significant increase in water and energy consumption. This phenomenon, combined with limited resource availability and current management practices, poses a major challenge to achieving sustainable water services. In this context, improving the energy efficiency of pressurized irrigation networks is essential, as it reduces energy consumption and operational costs, directly impacting the sustainability of the service. A critical factor influencing energy consumption, often overlooked, is the consideration of energy analysis during the layout definition stage of the distribution network. This study presents a methodology for designing more energy-efficient layouts in branched irrigation networks, using Machine Learning techniques, such as clustering and graph theory. The methodology is developed in five stages, leveraging information on the distribution of consumption points and potential pipeline routes. An initial sectorization is carried out, allowing the definition of a base topology from which various layouts of the main distribution network are identified and evaluated for energy efficiency. The proposed methodology enables the generation of energy-efficient branched layouts, optimizing the energy required at the head, adapting well to the terrain with minimal excess energy at the nodes, and minimizing overall costs. This approach represents a significant advancement toward achieving sustainable and energy-efficient irrigation networks. • Energy considerations are essential in the design of sustainable irrigation networks. • The methodology optimises the energy efficiency in the design of irrigation networks. • Optimal irrigation network design can be based on using Machine Learning.