An efficient improved photovoltaic irrigation system with artificial neural network based modeling of soil moisture distribution – A case study in Turkey

• Energy efficiency of solar system in irrigation system was improved.
• The optimum selection of panel the soil moisture distribution map obtained by the ANN.
• Hourly water demand and energy consumption were reduced in the orchard by 38%.
• The power of electric motor, battery and driver’s power were decreases.
• The outcome is reduces the number of solar panels and part of the initial setup costs.

Although recent years have witnessed excellent technological improvements, the initial setup costs of solar energy systems remain very high. It is for this reason that solar systems have not become sufficiently widespread. Optimum selection of panel power increases the applicability of these systems while at the same time decreasing their cost. The choice of which panel power is to be installed in a solar powered irrigation system will vary in direct proportion to the power of the pump supplying the water. In this study a reduction in pump power, and thus a reduction in the energy demand of the pump, is achieved by ensuring that the solar-powered drip irrigation system in an orchard is using water efficiently. To determine which areas needed to be irrigated, the authors used a soil moisture distribution map obtained via the artificial neural networks method. Using the system and software they developed to determine the soil moisture distribution, they were able to obtain an even distribution of water. Thus by preventing unnecessary irrigation, not only was instantaneous water demand reduced, but it was also possible to ensure the protection of freshwater resources. The system developed by the authors was observed to reduce the orchard’s daily water and energy consumption by 38%. Thus, using the method applied, it was possible to reduce the amount of pump power, depending on the instantaneous water demand, the total power of the solar panels, the current values of the electrical motor, the battery and power control units, and all other costs.