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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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070909Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2761
 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.
 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.
 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.
 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
 SA. Kalogirou, ÔÇÿÔÇÿArtificial Neural Networks in Renewable Energy Systems: A Review,-- Renewable & Sustainable Energy Reviews, 2001, Vol. 5, No. 4, pp. 373-401.
 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.
 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.
 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.
 Simon Haykin, Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice Hall, 1999.
 K.M.Hornik, M. Stinchcombe, H.White, ÔÇÿÔÇÿMultilayer Feedforward Networks are Universal Approximators,-- Neural Networks, 1989, 2(2):pp. 359-366.
 R. Fletcher. Practical ÔÇÿÔÇÿMethods of Optimization,-- 2nd ed. Wiley, Chichester, 1990.
 Cornelius T.Leondes, Neural Network Systems Techniques and Applications, Volume 1 ofNeural Network Systems architecture and applications, Academic Press, 1998.
 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.
 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).
 Hansen LK, Salamon P ÔÇÿÔÇÿNeural network ensembles,-- IEEE Trans Pattern Anal, 1990; 12(10):993-1001.
 D., Liew, A.C. and Chang, C.S., ÔÇÿÔÇÿA neural network short-term load forecaster,-- Electric Power Systems Research, 1994 , 28, pp. 227-234
 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.
 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.
 Azoff, E.M., ÔÇÿÔÇÿNeural Network Time Series Forecasting of Financial Markets,-- John Wiley and Sons, Chichester, 1994