NARX Based Short Term Forecasting of Water Flow Rate of a Photovoltaic Pumping System: A Case Study

Abstract

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 inputrecurrent neural network (NARXRNN) 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 NARXbased model is able to reach acceptable accuracy for 1–12 hrs (nextday) ahead predictions. The developed methodology provides valuable information to PVWPS operators for controlling the production, storage, and delivery of water.