Forecasting of Photovoltaic Power Generation by RBF Neural Networks

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Abstract:

Recent studies suggest that in order to facilitate higher market and grid penetration of solar power, the users need accurate forecasts of generating power from photovoltaic (PV) plants on multiple time horizons. Despite the large number of forecasting methods, the comparison of results and evaluation of relative advantages between models has been evasive. The general purpose of the paper is to explore the way of performing accurate forecasts of generating power from renewable energy sources so that independent system operators can act consequently. Different aspects of radial basis functions (RBF) neural networks (NNs) are discussed and an illustration of the proposed predictor software interface is given.

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200-205

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April 2014

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[1] W-J. Lee, Y. Liu, Y. Yang, and P. Wang, in: Forecasting power output of photovoltaic system based on weather classification and support vector machine, Industry applications society annual meeting (IAS), 2011, 1-6.

DOI: 10.1109/ias.2011.6074294

Google Scholar

[2] C. Tao, D. Shanxu, and C. Changson, in: Forecasting power output for grid – connected photovoltaic system without using solar radiation measurement, Power electronics for distributed generation systems (PEDG), IEEE, 2010, 773-777.

DOI: 10.1109/pedg.2010.5545754

Google Scholar

[3] A. Yona, T. Senjyu, and T. Funabashi, in: Application of recurrent neural network to short–term – ahead generating power forecasting for photovoltaic system, power engineering society general meeting, IEEE, 2007, 1-6.

DOI: 10.1109/pes.2007.386072

Google Scholar

[4] S.A. Kalogirou, in: Applications of artificial neural networks in energy systems: a review, Energy conversion management, 40(10), 1999, 1073- 1087.

DOI: 10.1016/s0196-8904(99)00012-6

Google Scholar

[5] H.T.C. Pedro, C.F.M. Coimbra, in: Assessment of forecasting techniques for solar power production with no exogenous inputs, Solar Energy 2012; 86(7): 2017e28.

DOI: 10.1016/j.solener.2012.04.004

Google Scholar

[6] S. Pelland, G. Galanis and G. Kallos, in: Solar and photovoltaic forecasting through post-processing of the global environmental multiscale numerical weather prediction model, Progress in Photovoltaics: Research and Applications 2013; 21(3): 284e96.

DOI: 10.1002/pip.1180

Google Scholar

[7] P. Bacher, H. Madsen and H.A. Nielsen, in: Online short-term solar power forecasting, Solar Energy 2009; 83(10): 1772e83.

DOI: 10.1016/j.solener.2009.05.016

Google Scholar

[8] P. Mandal, S. Madhira, A.U. Haque, J. Meng and R.L. Pineda, in: Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques, Procedia Computer Science 2012; 12: 332e7.

DOI: 10.1016/j.procs.2012.09.080

Google Scholar

[9] S.K. Chow, E.W. Lee and D.H. Li, in: Short-term prediction of photovoltaic energy generation by intelligent approach, Energy and Buildings 2012; 55: 660e7.

DOI: 10.1016/j.enbuild.2012.08.011

Google Scholar

[10] E. Lorenz, T. Scheidsteger, J. Hurka, D. Heinemann and C. Kurz, in: Regional PV power prediction for improved grid integration, Progress in Photovoltaics: Research and Applications 2011; 19(7): 757e71.

DOI: 10.1002/pip.1033

Google Scholar

[11] A. Yona, T. Senjyu, T. Funabashi and C-H. Kim, in: Determination method of insolation prediction with fuzzy and applying neural network for long-term ahead PV power output correction, IEEE Transactions on Sustainable Energy 2013; 4(2): 527e33.

DOI: 10.1109/tste.2013.2246591

Google Scholar

[12] S. Jafarzadeh, M. Fadali and C. Evrenosoglu, in: Solar power prediction using interval type-2 TSK modeling, IEEE Transactions on Sustainable Energy 2013; 4(2): 333e9.

DOI: 10.1109/tste.2012.2224893

Google Scholar

[13] G. Capizzi, F. Bonanno, in: A Wavelet Based Prediction of Wind and Solar Energy for Long-Term Simulation of Integrated Generation Systems, Proceedings of the 2010 International Conference on Modeling, Identification and Control, Okayama, Japan, July, (2010).

DOI: 10.1109/speedam.2010.5542259

Google Scholar

[14] K. Mitsuru, T. Akira and N. Yousuke, in: Forecasting Electric Power Generation in a Photovoltaic Power System for an Energy Network, IEEE Transactions on Power and Energy, Volume 127, Issue7, pp.847-853(2007).

DOI: 10.1541/ieejpes.127.847

Google Scholar

[15] A. Chaouachi, R. M. Kamel, R. Ichikawa, H. Hayashi and K. Nagasaka, in: Neural network ensemble-based solar power generation short-term forecasting, World Acad. Sci., Eng. Technol. 54 (2009), p.54–59.

Google Scholar

[16] Y-Z. Li, J-C. Niu, in: Forecast of power generation for grid-connected photovoltaic system based on grey model and Markov chain, 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp.1729-1733, June (2008).

DOI: 10.1109/iciea.2008.4582816

Google Scholar

[17] Y-Z. Li, J-C. Niu, in: Forecast of power generation for grid-connected photovoltaic system based on Markov chain, IEEE Asia-Pacific Power and Energy Engineering Conference, vol1, pp.652-655, (2009).

DOI: 10.1109/appeec.2009.4918386

Google Scholar

[18] Y-Z. Li, J-C. Niu, in: Short-Term Forecast of Power Generation for Grid-Connected Photovoltaic System Based on Advanced Grey-Markov Chain, Energy and Environment Technology, 2009 International Conference, Oct. 2009. pp: 275-278.

DOI: 10.1109/iceet.2009.305

Google Scholar