Short-term photovoltaic power forecasting with weighted support vector machine

IEEE International Conference on Automation and Logistics, ICAL

Volumn , Issue , 2012, Pages 248-253

  Xu, Ruidong ^a   Chen, Hao ^a   Sun, Xiaoyan ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

Author keywords

Photovoltaic; Power Forecasting; Similar Day; Weighted Support Vector Machine

Indexed keywords

ARTIFICIAL NEUTRAL NETWORK; GRID-CONNECTED PV POWER STATION; OUTPUT POWER; PHOTOVOLTAIC; PHOTOVOLTAIC POWER; POWER STATION; POWER SUPPLY; SIMILAR DAY; SOLAR PHOTOVOLTAICS; SUPPORTED VECTOR MACHINES; TIME INTERVAL; TRAINING SAMPLE; WEIGHTED SUPPORT VECTOR MACHINE;

PHOTOVOLTAIC CELLS; POWER PLANTS; SUN; SUPPORT VECTOR MACHINES;

FORECASTING;

EID: 84867925644     PISSN: 21618151     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAL.2012.6308206     Document Type: Conference Paper

References (17)

1. A. Yona, T. Senjyu, A. Y. Saber, T. Funabashi, H. Sekine and Kim. Chul-Hwan, "Application of neural network to 24-hour-ahead generating power forecasting for PV system, " Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1-6, 2008.
2. T. Cai, S. X. Duan, and C. S. Chen, "Forecasting power output for gridconnected photovoltaic power system without using Solar Radiation measurement, " 2010 2nd IEEE International Symposium on Power Electronics for Distributed Generation Systems, pp. 773-777, 2010
3. S. X. Wang, N. Zhang, Y. S. Zhao and J. Zhan. "Photovoltaic system power forecasting based on combined grey model and BP neural network, " International Conference on Electrical and Control Engineering (ICECE), Tianjin, China. 2011, pp. 4623-4626.
4. S. I. Sulaiman, T. K. Abdul Rahman, I. Musirin and S. Shaar "Optimizing three-layer neural network model for grid-connected photovoltaic system output prediction, " Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, 2009, pp. 338-343.
5. M. DING, N. Z. Xu "A method to forecast short-term output power of photovoltaic generation system based on Markov Chain, " Power System Technology, vol. 35, no. 1, pp. 152-157, 2011.
6. Y. Q. Zhu, J. Tian "Application of least square support vector machine in photovoltaic power forecasting, " Power System Technology, vol. 37, no. 7, pp. 54-59, 2011.
7. C. Cortes, V. Vapnik, "Support-vector networks, " Machine Learning, vol. 20, no. 2, pp. 273-297, 1995.
8. J. Fortuna, D. Capson, "Improved support vector classification using PCA and ICA feature space modification, " Pattern Recognition, vol. 37, no. 6, pp. 1117-1129, 2004.
9. O. Chapelle, P. Haffner, V. N. Vapnik, "Support vector machines for histogram-based image classification, " IEEE Transactions on Neural Networks, vol. 10 no. 5, pp. 1055-1064, 1999.
10. A. L. Oliveira, C. Baldisserotto and J. Baldisserotto. "A comparative study on support vector machine and constructive RBF neural network for prediction of success of dental implants, " Lecture Notes in Computer Science, 2005, 3773, pp. 1015-1026.
11. L. Guo, Y. X. Wu, L. Zhao T. Cao, W. L. Yan and X. Q. Shen, "Classification of mental task from EEG signals using immune feature weighted support vector machines, " IEEE Transactions on Magnetics, vol. 47, no. 5, pp. 866-869, 2011.
12. G. S. Hu, F. F. Zhu and Z. Ren. "Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines, " Expert Systems with Applications, vol. 35, no. 1, pp. 143-149, 2008.
13. X. Z. Wang, M. Han and J. Wang. "Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction, " Engineering Applications of Artificial Intelligence: The International Journal of Intelligent Real-Time Automation, vol. 23, no. 6, pp. 1012-1018, 2010.
14. X. L. Chen, P. Y. Liu, Z. F. Zhu and Y. Qiu, "A method of spam filtering based on weighted support vector machines, " The Second IEEE International Symposium on IT in Medicine and Education. Washington, DC: IEEE, 2009: pp. 947-950.
15. C. J. Li, X. H. Li, R. Zhao, J. L. Li and X. G. Liu, " A novel algorithm of selecting similar days for short-term power load forecasting, " Automation of Electric Power Systems, vol. 32, no. 9, pp. 69-73, 2008. .
16. J. Lorenzo, J. Mendez, M. Castrillon, and Daniel Hernández, "Short-term wind power forecast based on cluster analysis and artificial neural networks, " Lecture Notes in Computer Science, 2011, 6691: pp. 191-198.
17. C. S. Chen, S. X. Duan, T. Cai and B. Y. Liu, "Online 24-h solar power forecasting based on weather type classification using artificial neural network, " Solar Energy, vol. 85, no. 11, pp. 2856-2870, 2011.