Performance Analysis of Evolutionary ANN for Output Prediction of a Grid-Connected Photovoltaic System
This paper presents performance analysis of the
Evolutionary Programming-Artificial Neural Network (EPANN)
based technique to optimize the architecture and training parameters
of a one-hidden layer feedforward ANN model for the prediction of
energy output from a grid connected photovoltaic system. The ANN
utilizes solar radiation and ambient temperature as its inputs while the
output is the total watt-hour energy produced from the grid-connected
PV system. EP is used to optimize the regression performance of the
ANN model by determining the optimum values for the number of
nodes in the hidden layer as well as the optimal momentum rate and
learning rate for the training. The EPANN model is tested using two
types of transfer function for the hidden layer, namely the tangent
sigmoid and logarithmic sigmoid. The best transfer function, neural
topology and learning parameters were selected based on the highest
regression performance obtained during the ANN training and testing
process. It is observed that the best transfer function configuration for
the prediction model is [logarithmic sigmoid, purely linear].
[1] I. Ashraf and A. Chandra, "Artificial neural network based models for
forecasting electricity generation of grid connected solar PV power plant",
Int. Journal of Global Energy Issues, vol. 21, no. 1/2, pp. 119-130, 2004.
[2] M. Balzani and A. Reatti, "Neural network based model of a PV array for the
optimum performance of PV system", in Proc. PhD Research in
Microelectronics and Electronics Conf., vol. 2, 2005, pp. 123-126.
[3] X. Yao, "Evolving artificial neural networks", in Proc. Of the IEEE, vol. 87,
no. 9, 1999.
[4] S.I. Sulaiman, T.K. Abdul Rahman, and I. Musirin, "ANN-based technique
with embedded data filtering capability for predicting total AC power from
grid-connected photovoltaic system", in Proc. 2nd International Power
Engineering and Optimization Conference (PEOCO2008), 2008, pp.
272-277.
[5] X. Yao and Y. Liu, "Towards designing artificial neural networks by
evolution", Applied Mathematics and Computation, vol. 91, pp. 83-90,
1998.
[6] D.B. Fogel, "An introduction to simulated evolutionary optimization", IEEE
Transactions on Neural Networks, vol. 5, pp. 3-14, 1994.
[7] L.J. Fogel, "Autonomous automata", Industrial Research, vol. 4, no. 1,
pp.14-19, 1962.
[8] T. Back and H.-P. Schwefel, "Evolutionary computation: an overview", in
Proc. IEEE International Conference on Evolutionary Computation
(ICEC-96), 1996, pp. 20-29.
[9] M. Sarkar and B. Yegnanarayana, "Feedforward neural networks
configuration using evolutionary programming", in Proc. International
Conference on Neural Networks, vol. 1, 1997, pp. 438-443.
[10] K. Peng, S.S. Ge, and C. Wen, "An algorithm to determine neural network
hidden layer size and weight coefficients", in Proc. 15th IEEE International
Symposium on Intelligent Control (ISIC 2000), 2000, pp. 261-266.
[11] B. Yegnanarayana, Artificial Neural Networks, New Delhi: Prentice Hall of
India, 2006, ch. 1.
[12] S.R. Wenham, M.A. Green, and M.E. Watt. Applied Photovoltaics. Centre
for Photovoltaic Devices and Systems, Sydney: The University of New
South Wales, 1995, ch. 1.
[13] W.M. Jenkins, "Neural network weight training by mutation", Computers &
Structures, vol. 84, pp. 2107-2112, 2006.
[14] M. Annunziato, I. Bertini, A. Pannicelli and S. Pizzuti, "Evolutionary
feed-forward neural networks for traffic prediction", in Proc. International
Congress on Evolutionary Methods for Design, Optimization and Control
with Applications to Industrial Problems (EUROGEN 2003), 2003, pp.
1-8.
[15] J. Fang and Y. Xi, "Neural network design based on evolutionary
programming", Artificial Intelligence in Engineering, vol. 11, pp. 155-161,
1997.
[16] M.F. Augusteijn and T.P. Harrington, "Evolving transfer functions for
artificial neural networks", Neural Computing and Applications, vol. 13,
pp. 38-46, 2004.
[17] D. Johari, T.K. Abdul Rahman and I. Musirin, "Artificial Neural Network
Based Technique for Lightning Prediction", in Proc. 5th Student
Conference on Research and Development (SCOReD 2007), 2007.
[18] O.A. Dombaycr and M. Golcu, "Daily means ambient temperature
prediction using artificial neural network method: a case study of Turkey",
Renewable Energy, vol. 34, no. 4, pp. 1158-1161, 2009.
[19] W. Gao, "Study of new evolutionary neural network", in Proc. 2nd
International Conference on Machine Learning and Cybernetics, 2003,
pp. 1287-1292.
[20] I. Musirin and T.K. Abdul Rahman, "Evolutionary programming based
optimization technique for maximum loadability estimation in electric
power system", in Proc. National Power and Energy Conference (PECon),
2003, pp. 205-210.
[21] S.I. Sulaiman, T.K. Abdul Rahman and I. Musirin, "Semi automatic design
of two-hidden layer feedforward ANN for grid-photovoltaic system out
prediction", in Proc. International Graduate Conference of Engineering
and Science, 2008, pp. 91-96.
[22] F.I Hassim, I. Musirin and T.K. Abdul Rahman, "Voltage stability margin
enhancement using Evolutionary Programming (EP)", in Proc. 4th Student
Conference on Research and Development (SCOReD 2006), 2006, pp.
235-240.
[1] I. Ashraf and A. Chandra, "Artificial neural network based models for
forecasting electricity generation of grid connected solar PV power plant",
Int. Journal of Global Energy Issues, vol. 21, no. 1/2, pp. 119-130, 2004.
[2] M. Balzani and A. Reatti, "Neural network based model of a PV array for the
optimum performance of PV system", in Proc. PhD Research in
Microelectronics and Electronics Conf., vol. 2, 2005, pp. 123-126.
[3] X. Yao, "Evolving artificial neural networks", in Proc. Of the IEEE, vol. 87,
no. 9, 1999.
[4] S.I. Sulaiman, T.K. Abdul Rahman, and I. Musirin, "ANN-based technique
with embedded data filtering capability for predicting total AC power from
grid-connected photovoltaic system", in Proc. 2nd International Power
Engineering and Optimization Conference (PEOCO2008), 2008, pp.
272-277.
[5] X. Yao and Y. Liu, "Towards designing artificial neural networks by
evolution", Applied Mathematics and Computation, vol. 91, pp. 83-90,
1998.
[6] D.B. Fogel, "An introduction to simulated evolutionary optimization", IEEE
Transactions on Neural Networks, vol. 5, pp. 3-14, 1994.
[7] L.J. Fogel, "Autonomous automata", Industrial Research, vol. 4, no. 1,
pp.14-19, 1962.
[8] T. Back and H.-P. Schwefel, "Evolutionary computation: an overview", in
Proc. IEEE International Conference on Evolutionary Computation
(ICEC-96), 1996, pp. 20-29.
[9] M. Sarkar and B. Yegnanarayana, "Feedforward neural networks
configuration using evolutionary programming", in Proc. International
Conference on Neural Networks, vol. 1, 1997, pp. 438-443.
[10] K. Peng, S.S. Ge, and C. Wen, "An algorithm to determine neural network
hidden layer size and weight coefficients", in Proc. 15th IEEE International
Symposium on Intelligent Control (ISIC 2000), 2000, pp. 261-266.
[11] B. Yegnanarayana, Artificial Neural Networks, New Delhi: Prentice Hall of
India, 2006, ch. 1.
[12] S.R. Wenham, M.A. Green, and M.E. Watt. Applied Photovoltaics. Centre
for Photovoltaic Devices and Systems, Sydney: The University of New
South Wales, 1995, ch. 1.
[13] W.M. Jenkins, "Neural network weight training by mutation", Computers &
Structures, vol. 84, pp. 2107-2112, 2006.
[14] M. Annunziato, I. Bertini, A. Pannicelli and S. Pizzuti, "Evolutionary
feed-forward neural networks for traffic prediction", in Proc. International
Congress on Evolutionary Methods for Design, Optimization and Control
with Applications to Industrial Problems (EUROGEN 2003), 2003, pp.
1-8.
[15] J. Fang and Y. Xi, "Neural network design based on evolutionary
programming", Artificial Intelligence in Engineering, vol. 11, pp. 155-161,
1997.
[16] M.F. Augusteijn and T.P. Harrington, "Evolving transfer functions for
artificial neural networks", Neural Computing and Applications, vol. 13,
pp. 38-46, 2004.
[17] D. Johari, T.K. Abdul Rahman and I. Musirin, "Artificial Neural Network
Based Technique for Lightning Prediction", in Proc. 5th Student
Conference on Research and Development (SCOReD 2007), 2007.
[18] O.A. Dombaycr and M. Golcu, "Daily means ambient temperature
prediction using artificial neural network method: a case study of Turkey",
Renewable Energy, vol. 34, no. 4, pp. 1158-1161, 2009.
[19] W. Gao, "Study of new evolutionary neural network", in Proc. 2nd
International Conference on Machine Learning and Cybernetics, 2003,
pp. 1287-1292.
[20] I. Musirin and T.K. Abdul Rahman, "Evolutionary programming based
optimization technique for maximum loadability estimation in electric
power system", in Proc. National Power and Energy Conference (PECon),
2003, pp. 205-210.
[21] S.I. Sulaiman, T.K. Abdul Rahman and I. Musirin, "Semi automatic design
of two-hidden layer feedforward ANN for grid-photovoltaic system out
prediction", in Proc. International Graduate Conference of Engineering
and Science, 2008, pp. 91-96.
[22] F.I Hassim, I. Musirin and T.K. Abdul Rahman, "Voltage stability margin
enhancement using Evolutionary Programming (EP)", in Proc. 4th Student
Conference on Research and Development (SCOReD 2006), 2006, pp.
235-240.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61813", author = "S.I Sulaiman and T.K Abdul Rahman and I. Musirin and S. Shaari", title = "Performance Analysis of Evolutionary ANN for Output Prediction of a Grid-Connected Photovoltaic System", abstract = "This paper presents performance analysis of the
Evolutionary Programming-Artificial Neural Network (EPANN)
based technique to optimize the architecture and training parameters
of a one-hidden layer feedforward ANN model for the prediction of
energy output from a grid connected photovoltaic system. The ANN
utilizes solar radiation and ambient temperature as its inputs while the
output is the total watt-hour energy produced from the grid-connected
PV system. EP is used to optimize the regression performance of the
ANN model by determining the optimum values for the number of
nodes in the hidden layer as well as the optimal momentum rate and
learning rate for the training. The EPANN model is tested using two
types of transfer function for the hidden layer, namely the tangent
sigmoid and logarithmic sigmoid. The best transfer function, neural
topology and learning parameters were selected based on the highest
regression performance obtained during the ANN training and testing
process. It is observed that the best transfer function configuration for
the prediction model is [logarithmic sigmoid, purely linear].", keywords = "Artificial neural network (ANN), Correlation
coefficient (R), Evolutionary programming-ANN (EPANN),
Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.", volume = "3", number = "5", pages = "1213-7", }
