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.

References

References
1.
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
.
2.
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
.
3.
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
.
4.
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
.
5.
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
.
6.
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
.
7.
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
.
8.
Altunkaynak
,
A.
,
2007
, “
Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks
,”
Water Resour. Manage.
,
21
(
2
), pp.
399
408
.
9.
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
.
10.
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
.
11.
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
.
12.
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
.
13.
Napoli
,
R.
, and
Piroddi
,
L.
,
2010
, “
Nonlinear Active Noise Control With NARX Models
,”
IEEE Trans. Speech Audio Process.
,
18
(
2
), pp.
286
295
.
14.
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
.
15.
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
.
16.
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
.
17.
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
.
You do not currently have access to this content.