Paper:

# Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting

## Aymen Chaouachi, Rashad M. Kamel, and Ken Nagasaka

Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nagasaka Laboratory, 2-24-16, Nakamachi, Koganei, Tokyo 184-8588, Japan

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.14 No.1, pp. 69-75, 2010.

- [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 Feedforward 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.