Comparison of ANN Models for Estimating Optimal Points of Crystalline Silicon Photovoltaic Modules
Author:
Affiliation:
1. Department of Computer Science and Electrical Engineering of Kumamoto University
2. Department of Electrical and Electronics Engineering of Ege University
Publisher
Institute of Electrical Engineers of Japan (IEE Japan)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Link
https://www.jstage.jst.go.jp/article/ieejpes/130/7/130_7_661/_pdf
Reference25 articles.
1. (1) A. Mellit and S. A. Kalogirou: Artificial intelligence techniques for photovoltaic applications: A review”, Progress in Energy & Combustion Sci., Vol. 34, No. 5, pp. 574-632 (2008)
2. (2) A. Mellit, S. A. Kalogirou, L. Hontoria, and S. Shaari: “Artificial intelligence techniques for sizing photovoltaic systems: A review”, Renewable & Sustainable Energy Rev., Vol. 13, No. 2, pp. 406-419 (2009)
3. (3) F. Giraud and Z. M. Salameh: “Analysis of the effects of a passing cloud on a grid-interactive photovoltaic system with battery storage using neural networks”, IEEE Trans. Energy Conversion, Vol. 14, No. 4, pp. 1572-1577 (1999)
4. (4) A. Mellit, M. Benghanem, and S. A. Kalogirou: “An adaptive wavelet-network model for forecasting daily total solar-radiation”, Appl. Energy, Vol. 83, No. 7, pp. 705-722 (2006)
5. (5) X. Sun, W. Wu, X. Li, and Q. Zhao: “A research on photovoltaic energy controlling system with maximum power point tracking”, Proc. 2002 Power Conversion Conf., Vol. 2, pp. 822-826 (2002)
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