Hourly water flow rate (HWFR) forecasting is very important to photovoltaic water pumping system (PVWPS) planning, operation, and control. In this paper, a nonlinear autoregressive with exogenous input-recurrent neural network (NARX-RNN) is investigated for the prediction of water flow rate (WFR) using experimental data collected from a PVWPS installed at Madinah site (Saudi Arabia). Results showed that the developed NARX-based model is able to reach acceptable accuracy for 1–12 hrs (next-day) ahead predictions. The developed methodology provides valuable information to PVWPS operators for controlling the production, storage, and delivery of water.
Research and current status of the solar photovoltaic water pumping system – A review