Neural network ensemble-based solar power generation short-term forecasting

Journal of Advanced Computational Intelligence and Intelligent Informatics

Volumn 14, Issue 1, 2010, Pages 69-75

  Chaouachi, Aymen ^a   Kamel, Rashad M ^a   Nagasaka, Ken ^a  

^a TOKYO UNIVERSITY OF AGRICULTURE AND TECHNOLOGY   (Japan)

Author keywords

Neural network ensemble; Neural networks; Short term forecasting; Solar power generation

Indexed keywords

EID: 77749237383     PISSN: 13430130     EISSN: 18838014     Source Type: Journal    
DOI: 10.20965/jaciii.2010.p0069     Document Type: Conference Paper

References (25)

1. S. K. Reddy and R. Manish,"Solar resource estimation using artificial neural networks and comparison with other correlation models,"Energy Conversion and Management, Vol.44, pp. 2519-2530, 2003.
2. M. Mohandes, A. Balghonaim, M. Kassas, H. S. Rehman,"Use of radial basis functions for estimating monthly mean daily solar radiation,"Sol Energy, Vol.68, No.2, pp. 161-168, 2000.
3. A. Mellit and S. A. Kalogirou,"Artificial intelligence techniques for photovoltaic applications: a review,"Progress in Energy and Combustion Science, Vol.34, pp. 574-632, 2008.
4. A. Mellit,"Artificial Intelligence technique for modeling and forecasting of solar radiation data: a review,"Int. J. of Artificial Intelligence and Soft Computing, Vol.1, Issue 1, pp. 52-76, 2008.
5. S. A. Kalogirou,"Artificial Neural Networks in Renewable Energy Systems: A Review,"Renewable &Sustainable Energy Reviews, Vol.5, No.4, pp. 373-401, 2001.
6. C. Maher and B. A. Mohsen,"Neuro-Fuzzy Dynamic Model with Kalman Filter to Forecast Irradiance and Temperature for Solar Energy Systems,"Renew Energy, pp. 1435-1443, 2008.
7. J. C. Cao and S. H. Cao,"Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis,"Energy Vol.3, pp. 13435-13445, 2006.
8. Y. Jiang,"Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models,"Energy policy, Vol.36, No.10, pp. 3833-3837, 2008.
9. S. I. Sulaiman, T. K. A. Rahman, I. Musirin, and S. Shaari, "Performance analysis of Two-variate ANN Models for predicting the output Power from Grid-connected Photovoltaic System,"Int. J. of power, Energy and Artificial Intelligence, Vol.2, No.1, pp. 73-77, 2009.
10. M. Kawaguchi, S. Ichikawa, M. Okuno, T. Jimbo, and N. Ishii,"Prediction of Electric Power Generation of Solar Cell Using the Neural Network,"ISSN 0302-9743, Vol.4253, pp. 387-392, 2006.
11. S. Haykin,"Neural Networks,"A Comprehensive Foundation, 2nd Edition, Prentice Hall, 1999.
12. K. M. Hornik, M. Stinchcombe, and H. White,"Multilayer Feed-forward Networks are Universal Approximators,"Neural Networks, Vol.2, No.2, pp. 359-366, 1989.
13. R. Fletcher,"Practical Methods of Optimization,"2nd ed. Wiley, Chichester, 1990.
14. C. T. Leondes,"Neural Network Systems Techniques and Applications,"Vol.1 of Neural Network Systems architecture and applications, Academic Press, 1998.
15. E. J. Hartman, J. D. Keeler, and J. M. Kowalski,"Layered neural networks with gaussian hidden units as universal approximators,"Neural Comput, 2, pp. 210-215, 1990.
16. J. L. Elman,"Distributed representations, simple recurrent networks and grammatical structure,"Mach. Learn., 7(2/3), pp. 195-226, 1991.
17. Z. Gao, F. Ming, and Z. Hongling,"Bagging Neural Networks for Predicting Water Consumption,"J. of Communication and Computer, Vol.2, No.3, (Serial No.4), 2005.
18. L. K. Hansen and P. Salamon,"Neural network ensembles,"IEEE Trans Pattern Anal, 12(10), pp. 993-1001, 1990.
19. D. Srinivasan, A. C. Liew, and C. S. Chang,"A neural network short-term load forecaster,"Electric Power Systems Research, 28, pp. 227-234, 1994.
20. J. Sola and J. Sevilla,"Importance of data normalization for the application of neural networks to complex industrial problems,"IEEE Trans. on Nuclear Science, 44(3), pp. 1464-1468, 1997.
21. G. Zhang, B. E. Patuwo, and M. Y. Hu,"Forecasting with artificial neural networks: The state of the art,"Int. J. of Forecasting, Vol. 14, Issue 1, pp. 35-62, 1998.
22. E.M. Azoff,"Neural Network Time Series Forecasting of Financial Markets,"John Wiley and Sons, Chichester, 1994.
23. J. Paetz,"Reducing the number of neurons in radial basis function networks with dynamic decay adjustment,"Neuro-computing 62, pp. 79-91, 2004.
24. R. Perfetti and E. Ricci,"Reduced complexity RBF classifiers with support vector centres and dynamic decay adjustment,"Neurocomputing, pp. 2446-2450, 2006.
25. E. Ricci and R. Perfetti,"Improved pruning strategy for radial basis function networks with dynamic decay adjustment,"Neurocomputing, pp. 1728-1732, 2006.