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.
NARX-Based Short-Term Forecasting of Water Flow Rate of a Photovoltaic Pumping System: A Case Study
Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received May 21, 2015; final manuscript received October 12, 2015; published online November 25, 2015. Assoc. Editor: M. Keith Sharp.
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Haddad, S., Mellit, A., Benghanem, M., and Daffallah, K. O. (November 25, 2015). "NARX-Based Short-Term Forecasting of Water Flow Rate of a Photovoltaic Pumping System: A Case Study." ASME. J. Sol. Energy Eng. February 2016; 138(1): 011004. https://doi.org/10.1115/1.4031970
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