0
Research Papers

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

[+] Author and Article Information
Sofiane Haddad

Electronic Department,
Faculty of Sciences and Technology,
University of Jijel,
BP 157 Taher,
Jijel 18002, Algeria
e-mail: sof_had@yahoo.fr

Adel Mellit

Renewable Energy Laboratory,
Faculty of Sciences and Technology,
University of Jijel,
BP 98 Ouled Issa,
Jijel 18000, Algeria
e-mail: adelmellit2013@gmail.com

Mohamed Benghanem

Physics Department,
Faculty of Science,
Taibah University,
P.O. Box 344,
Madinah 274, Saudi Arabia
e-mail: benghanem_mohamed@yahoo.fr

Khalid Osman Daffallah

Physics Department,
Faculty of Science,
Taibah University,
P.O. Box 344,
Madinah 274, Saudi Arabia
e-mail: khalid_71@hotmail.com

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.

J. Sol. Energy Eng 138(1), 011004 (Nov 25, 2015) (6 pages) Paper No: SOL-15-1152; doi: 10.1115/1.4031970 History: Received May 21, 2015; Revised October 12, 2015

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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Caracas, J. V. M. , Farias, G. C. , Teixeira, L. F. M. , and Ribeiro, L. A. S. , 2014, “ Implementation of a High-Efficiency, High-Lifetime, and Low-Cost Converter for an Autonomous Photovoltaic Water Pumping System,” IEEE Trans. Ind. Appl., 50(1), pp. 631–641. [CrossRef]
Faldella, E. , Cardinali, G. C. , and Calzolari Pier Ugo, 1991, “ Architectural and Design Issues on Optimal Management of Photovoltaic Pumping Systems,” IEEE Trans. Ind. Electron., 38(5), pp. 385–392. [CrossRef]
Kolhe, M. , Joshi, J. C. , and Kothari, D. P. , 2004, “ Performance Analysis of a Directly Coupled Photovoltaic Water-Pumping System,” IEEE Trans. Energy Conv., 19(3), pp. 613–618. [CrossRef]
Elgendy, M. A. , Zahawi, B. , and Atkinson, D. J. , 2010, “ Comparison of Directly Connected and Constant Voltage Controlled Photovoltaic Pumping Systems,” IEEE Trans. Sustainable Energy, 1(3), pp. 184–192. [CrossRef]
Elgendy, M. A. , Zahawi, B. , and Atkinson, D. J. , 2012, “ Assessment of Perturb and Observe MPPT Algorithm Implementation Techniques for PV Pumping Applications,” IEEE Trans. Sustainable Energy, 3(1), pp. 21–33. [CrossRef]
Habiballahi, M. , Ameri, M. , and Mansouri, S. H. , 2015, “ Efficiency Improvement of Photovoltaic Water Pumping Systems by Means of Water Flow Beneath Photovoltaic Cells Surface,” ASME J. Sol. Energy Eng., 137(4), p. 044501. [CrossRef]
Vaziri, M. , 1997, “ Predicting Caspian Sea Surface Water Level by ANN and ARIMA Models,” J. Waterway Port Coastal Ocean Eng., 123(4), pp. 158–162. [CrossRef]
Altunkaynak, A. , 2007, “ Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks,” Water Resour. Manage., 21(2), pp. 399–408. [CrossRef]
Nayak, P. C. , Sudheer, K. P. , Rangan, D. M. , and Ramasastri, K. S. , 2004, “ A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series,” J. Hydrol., 291(1), pp. 52–66. [CrossRef]
Gueldal, V. , and Tongal, H. , 2010, “ Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in E˘girdir Lake Level Forecasting,” Water Resour. Manage., 24(1), pp. 105–128. [CrossRef]
Talebizadeh, M. , and Moridnejad, A. , 2011, “ Uncertainty Analysis for the Forecast of Lake Level Fluctuations Using Ensembles of ANN and ANFIS Models,” Expert Syst. Appl., 38(4), pp. 4126–4135. [CrossRef]
Cornaro, C. , Bucci, F. , Pierro, M. , Del Frate, F. , Peronaci, S. , and Taravat, A. , 2015, “ Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction,” ASME J. Sol. Energy Eng., 137(3), p. 031011. [CrossRef]
Napoli, R. , and Piroddi, L. , 2010, “ Nonlinear Active Noise Control With NARX Models,” IEEE Trans. Speech Audio Process., 18(2), pp. 286–295. [CrossRef]
Chen, S. , Wang, X. X. , and Harris, C. J. , 2008, “ NARX-Based Nonlinear System Identification Using Orthogonal Least Squares Basis Hunting,” IEEE Trans. Control Syst. Technol., 16(1), pp. 78–84. [CrossRef]
Xiao, Z. , Jing, X. , and Cheng, L. , 2013, “ Parameterized Convergence Bounds for Volterra Series Expansion of NARX Models,” IEEE Trans. Signal Process., 61(20), pp. 5026–5038. [CrossRef]
Benghanem, M. , Daffallah, K. O. , Joraid, A. A. , Alamri, S. N. , and Jaber, A. , 2013, “ Performances of Solar Water Pumping System Using Helical Pump for a Deep Well: A Case Study for Madinah, Saudi Arabia,” Energy Conv. Manage., 65, pp. 50–56. [CrossRef]
Haddad, S. , Benghanem, M. , Mellit, A. , and Daffallah, K. O. , 2015, “ ANNs-Based Modeling and Prediction of Hourly Flow Rate of a Photovoltaic Water Pumping System: Experimental Validation,” Renewable Sustainable Energy Rev., 43, pp. 635–643. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Photograph of the real PVWPS

Grahic Jump Location
Fig. 2

Measured solar irradiation from 1st June to 12th July (42 days)

Grahic Jump Location
Fig. 3

Measured air temperature from 1st June to 12th July (42days)

Grahic Jump Location
Fig. 4

Measured HWFR from 1st June to 12th July (42 days)

Grahic Jump Location
Fig. 5

Flowchart of the proposed methodology for forecasting the WFR multihours ahead of a PVWP system

Grahic Jump Location
Fig. 6

Real and forecasted HWFR, next-day 12-hrs ahead (12th July)

Grahic Jump Location
Fig. 7

Real versus forecast from 1 hr to 12 hrs ahead

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In