Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting
This paper presents the applicability of artificial
neural networks for 24 hour ahead solar power generation forecasting
of a 20 kW photovoltaic system, the developed forecasting is suitable
for a reliable Microgrid energy management. In total four neural
networks were proposed, namely: multi-layred perceptron, radial
basis function, recurrent and a neural network ensemble consisting in
ensemble of bagged networks. Forecasting reliability of the proposed
neural networks was carried out in terms forecasting error
performance basing on statistical and graphical methods. The
experimental results showed that all the proposed networks achieved
an acceptable forecasting accuracy. In term of comparison the neural
network ensemble gives the highest precision forecasting comparing
to the conventional networks. In fact, each network of the ensemble
over-fits to some extent and leads to a diversity which enhances the
noise tolerance and the forecasting generalization performance
comparing to the conventional networks.
[1] Reddy K.S. and Manish R, ÔÇÿÔÇÿSolar resource estimation using artificial
neural networks and comparison with other correlation models,-- Energy
Conversion and Management, 2003, Vol. 44, pp.2519-2530.
[2] Mohandes M, Balghonaim A, Kassas M, Rehman S, Halawani. ÔÇÿÔÇÿUse of
radial basis functions for estimating monthly mean daily solar
radiation;-- Sol Energy, 2000;68(2):161-8.
[3] Mellit, A. and Kalogirou, S.A. ÔÇÿÔÇÿArtificial intelligence techniques for
photovoltaic applications: a review,-- Progress in Energy and
Combustion Science, 2008, Vol. 34, pp.574-632.[4] A. Mellit ÔÇÿÔÇÿArtificial Intelligence technique for modeling and forecasting
of solar radiation data: a review,-- International Journal of Artificial
Intelligence and Soft Computing, 2008, Volume 1 , Issue 1, pp 52-76
[5] SA. Kalogirou, ÔÇÿÔÇÿArtificial Neural Networks in Renewable Energy
Systems: A Review,-- Renewable & Sustainable Energy Reviews, 2001,
Vol. 5, No. 4, pp. 373-401.
[6] CHAABENE Maher, BEN AMMAR Mohsen, ÔÇÿÔÇÿNeuro-Fuzzy Dynamic
Model with Kalman Filter to Forecast Irradiance and Temperature for
Solar Energy Systems,-- Renew Energy, 2008, pages 1435-1443.
[7] J.C Cao,. and, S.H. Cao ÔÇÿÔÇÿStudy of forecasting solar irradiance using
neural networks with preprocessing sample data by wavelet analysis-,
Energy, 2006, Vol. 3, pp.13435-13445.
[8] YINGNI JIANG, ÔÇÿÔÇÿPrediction of monthly mean daily diffuse solar
radiation using artificial neural networks and comparison with other
empirical models,-- Energy policy, 2008, vol.36,n10,pp.3833-3837.
[9] Simon Haykin, Neural Networks. A Comprehensive Foundation, 2nd
Edition, Prentice Hall, 1999.
[10] K.M.Hornik, M. Stinchcombe, H.White, ÔÇÿÔÇÿMultilayer Feedforward
Networks are Universal Approximators,-- Neural Networks, 1989,
2(2):pp. 359-366.
[11] R. Fletcher. Practical ÔÇÿÔÇÿMethods of Optimization,-- 2nd ed. Wiley,
Chichester, 1990.
[12] Cornelius T.Leondes, Neural Network Systems Techniques and
Applications, Volume 1 ofNeural Network Systems architecture and
applications, Academic Press, 1998.
[13] E. J. Hartman, J. D. Keeler, and J. M. Kowalski, ÔÇÿÔÇÿLayered neural
networks with gaussian hidden units as universal approximators,--
Neural Comput, 1990, 2:210-215.
[14] ZHANG Gao, FAN Ming, ZHAO Hongling, ÔÇÿÔÇÿBagging Neural
Networks for Predicting Water Consumption,-- Journal of
Communication and Computer, 2005, Volume 2, No.3 (Serial No.4).
[15] Hansen LK, Salamon P ÔÇÿÔÇÿNeural network ensembles,-- IEEE Trans
Pattern Anal, 1990; 12(10):993-1001.
[16] D., Liew, A.C. and Chang, C.S., ÔÇÿÔÇÿA neural network short-term load
forecaster,-- Electric Power Systems Research, 1994 , 28, pp. 227-234
[17] J. Sola and J. Sevilla, ÔÇÿÔÇÿImportance of data normalization for the
application of neural networks to complex industrial problems,-- IEEE
Transactions on Nuclear Science, 1997, 44(3) 1464-1468.
[18] Guoqiang Zhang, B. Eddy Patuwo and Michael Y. Hu, ÔÇÿÔÇÿForecasting
with artificial neural networks:The state of the art,-- International
Journal of Forecasting, 1998, Volume 14, Issue 1, Pages 35-62.
[19] Azoff, E.M., ÔÇÿÔÇÿNeural Network Time Series Forecasting of Financial
Markets,-- John Wiley and Sons, Chichester, 1994
[1] Reddy K.S. and Manish R, ÔÇÿÔÇÿSolar resource estimation using artificial
neural networks and comparison with other correlation models,-- Energy
Conversion and Management, 2003, Vol. 44, pp.2519-2530.
[2] Mohandes M, Balghonaim A, Kassas M, Rehman S, Halawani. ÔÇÿÔÇÿUse of
radial basis functions for estimating monthly mean daily solar
radiation;-- Sol Energy, 2000;68(2):161-8.
[3] Mellit, A. and Kalogirou, S.A. ÔÇÿÔÇÿArtificial intelligence techniques for
photovoltaic applications: a review,-- Progress in Energy and
Combustion Science, 2008, Vol. 34, pp.574-632.[4] A. Mellit ÔÇÿÔÇÿArtificial Intelligence technique for modeling and forecasting
of solar radiation data: a review,-- International Journal of Artificial
Intelligence and Soft Computing, 2008, Volume 1 , Issue 1, pp 52-76
[5] SA. Kalogirou, ÔÇÿÔÇÿArtificial Neural Networks in Renewable Energy
Systems: A Review,-- Renewable & Sustainable Energy Reviews, 2001,
Vol. 5, No. 4, pp. 373-401.
[6] CHAABENE Maher, BEN AMMAR Mohsen, ÔÇÿÔÇÿNeuro-Fuzzy Dynamic
Model with Kalman Filter to Forecast Irradiance and Temperature for
Solar Energy Systems,-- Renew Energy, 2008, pages 1435-1443.
[7] J.C Cao,. and, S.H. Cao ÔÇÿÔÇÿStudy of forecasting solar irradiance using
neural networks with preprocessing sample data by wavelet analysis-,
Energy, 2006, Vol. 3, pp.13435-13445.
[8] YINGNI JIANG, ÔÇÿÔÇÿPrediction of monthly mean daily diffuse solar
radiation using artificial neural networks and comparison with other
empirical models,-- Energy policy, 2008, vol.36,n10,pp.3833-3837.
[9] Simon Haykin, Neural Networks. A Comprehensive Foundation, 2nd
Edition, Prentice Hall, 1999.
[10] K.M.Hornik, M. Stinchcombe, H.White, ÔÇÿÔÇÿMultilayer Feedforward
Networks are Universal Approximators,-- Neural Networks, 1989,
2(2):pp. 359-366.
[11] R. Fletcher. Practical ÔÇÿÔÇÿMethods of Optimization,-- 2nd ed. Wiley,
Chichester, 1990.
[12] Cornelius T.Leondes, Neural Network Systems Techniques and
Applications, Volume 1 ofNeural Network Systems architecture and
applications, Academic Press, 1998.
[13] E. J. Hartman, J. D. Keeler, and J. M. Kowalski, ÔÇÿÔÇÿLayered neural
networks with gaussian hidden units as universal approximators,--
Neural Comput, 1990, 2:210-215.
[14] ZHANG Gao, FAN Ming, ZHAO Hongling, ÔÇÿÔÇÿBagging Neural
Networks for Predicting Water Consumption,-- Journal of
Communication and Computer, 2005, Volume 2, No.3 (Serial No.4).
[15] Hansen LK, Salamon P ÔÇÿÔÇÿNeural network ensembles,-- IEEE Trans
Pattern Anal, 1990; 12(10):993-1001.
[16] D., Liew, A.C. and Chang, C.S., ÔÇÿÔÇÿA neural network short-term load
forecaster,-- Electric Power Systems Research, 1994 , 28, pp. 227-234
[17] J. Sola and J. Sevilla, ÔÇÿÔÇÿImportance of data normalization for the
application of neural networks to complex industrial problems,-- IEEE
Transactions on Nuclear Science, 1997, 44(3) 1464-1468.
[18] Guoqiang Zhang, B. Eddy Patuwo and Michael Y. Hu, ÔÇÿÔÇÿForecasting
with artificial neural networks:The state of the art,-- International
Journal of Forecasting, 1998, Volume 14, Issue 1, Pages 35-62.
[19] Azoff, E.M., ÔÇÿÔÇÿNeural Network Time Series Forecasting of Financial
Markets,-- John Wiley and Sons, Chichester, 1994
@article{"International Journal of Electrical, Electronic and Communication Sciences:57525", author = "A. Chaouachi and R.M. Kamel and R. Ichikawa and H. Hayashi and K. Nagasaka", title = "Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting", abstract = "This paper presents the applicability of artificial
neural networks for 24 hour ahead solar power generation forecasting
of a 20 kW photovoltaic system, the developed forecasting is suitable
for a reliable Microgrid energy management. In total four neural
networks were proposed, namely: multi-layred perceptron, radial
basis function, recurrent and a neural network ensemble consisting in
ensemble of bagged networks. Forecasting reliability of the proposed
neural networks was carried out in terms forecasting error
performance basing on statistical and graphical methods. The
experimental results showed that all the proposed networks achieved
an acceptable forecasting accuracy. In term of comparison the neural
network ensemble gives the highest precision forecasting comparing
to the conventional networks. In fact, each network of the ensemble
over-fits to some extent and leads to a diversity which enhances the
noise tolerance and the forecasting generalization performance
comparing to the conventional networks.", keywords = "Neural network ensemble, Solar power generation,24 hour forecasting, Comparative study", volume = "3", number = "6", pages = "1334-6", }
