Browse > Article
http://dx.doi.org/10.5370/KIEEP.2015.64.4.246

Development of PV Power Prediction Algorithm using Adaptive Neuro-Fuzzy Model  

Lee, Dae-Jong (Dept. of Electrical Engineering Korea National University of Transportation)
Lee, Jong-Pil (Dept. of Electrical Engineering Korea National University of Transportation)
Lee, Chang-Sung (Dept. of Electrical Engineering Korea National University of Transportation)
Lim, Jae-Yoon (Dept. of Computer Electronics Daeduk College)
Ji, Pyeong-Shik (Dept. of Electrical Engineering Korea National University of Transportation)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers P / v.64, no.4, 2015 , pp. 246-250 More about this Journal
Abstract
Solar energy will be an increasingly important part of power generation because of its ubiquity abundance, and sustainability. To manage effectively solar energy to power system, it is essential part In this paper, we develop the PV power prediction algorithm using adaptive neuro-fuzzy model considering various input factors such as temperature, solar irradiance, sunshine hours, and cloudiness. To evaluate performance of the proposed model according to input factors, we performed various experiments by using real data.
Keywords
PV power; Prediction model; ANFIS; Data selection;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 A. Molki, "Dust affects solar cell efficiency," Physics Education, Vol. 45, pp. 456-458, 2010.   DOI
2 C. H. Henry, "Limiting efficiencies of ideal single and multiple energy gap terrestrial solar cells," J. App. Phys. Vol. 51, pp. 4494, 1980.   DOI
3 J. Hedstrom, J. Kessler, M. Ruckh, K. O. Velthaus, Hans-Werner Schock, "ZnO/CdS/CuInSe2 thin-film Solar cells with improved performance," Applied Physics Letters, Vol. 62, No. 6, pp. 597-599, 1993.   DOI
4 S. R. Kurtz. D. Myers. T. Townsend, C. Whitaker, A. Maish, R. Hulstrom, K. Emery, "Outdoor rating conditions for photovoltaic modules and systems," Solar Energy Materials & Solar Cells, Vol. 62, pp. 379-391, 2000.   DOI
5 D. R. Myers, S. R. Kurtz, C. Whitaker, T. Townsend, "Preliminary Investigations of Outdoor Meteorological Broadband and Spectral Conditions for Evaluating Photovoltaic Modules and systems," Program and Proceedings : NCPV Program Review Meeting 2000, pp. 16-19, 2000.
6 C. S. Chin, A. Babu, W. McBride, "Design, modeling and testing of a standalone single axis active solar tracker using MATLAB/Simulink," Renewable Energy, vol. 36, no. 11, pp. 3075-3090, 2011.   DOI
7 Y. S. Heo, J. G. Kim, B. M. Kwon, H. J. Song, "Prediction and Analysis of Photovoltaic Modules's Output using MATLAB," Journal of academia-industrial technology, Vol. 11, No. 8, pp. 2963-2967, 2010.
8 Modeling and fault diagnosis of a photovoltaic systems," Electrial Power Research, Vol. 78, No. 1, pp. 97-105, 2008.   DOI
9 Hyun Cheol Cho, "A Study on Dynamic Modeling of Photovoltaic Power Generator Systems using Probability and Statistics Theories," The Transactions of the Korean Institute of Electrical Engineers, vol. 61, no. 7, pp. 1007-1013, 2012.   DOI
10 H. C. Cho, Y. J. Jung, "Probabilistic Modeling of Photovoltaic Power Systems with Big Learning Data Sets," Journal of Korean Institute of Intelligent Systems, Vol. 23, No. 5, pp. 412-417, 2013.   DOI
11 J. J. Song, Y. S. Jeong S. H. Lee, "Analysis of prediction model for solar power generation," Journal of Digital Convergence, Vol. 12, No. 3, pp. 243-248, 2014.   DOI
12 Kim Kwang-Deuk, "The Development of the Short-Term Predict Model for Solar Power Generation," Journal of the Korean Solar Energy Society, Vol. 33, No. 6, pp. 62-69. 2013   DOI
13 J. S. R. Jang, "ANFIS:Adaptive-network-based fuzzy inference system," IEEE Trans. on Systems, Man and Cybernetics, Vol. 23, No. 3, pp. 665-685, 1993.   DOI