Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques

Procedia Computer Science

Volumn 12, Issue , 2012, Pages 332-337

  Mandal, Paras ^a   Madhira, Surya Teja Swarroop ^b   Ul haque, Ashraf ^c   Meng, Julian ^c   Pineda, Ricardo L ^a  

^a UNIVERSITY OF TEXAS AT EL PASO   (United States)

^b University of Texas at El Paso   (United States)

^c UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

Artificial intelligence; Renewable energy; Solar photovoltaic power forecasting; Wavelet transform

Indexed keywords

EID: 84880412487     PISSN: 18770509     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2012.09.080     Document Type: Conference Paper

References (11)

1. W-J. Lee, Y. Liu, Y. Yang, and P. Wang, Forecasting power output of photovoltaic system based on weather classification and support vector machine, Industry applications society annual meeting (IAS), 2011, 1-6.
2. C. Tao, D. Shanxu, and C. Changson, Forecasting power output for grid - connected photovoltaic system without using solar radiation measurement, Power electronics for distributed generation systems (PEDG), IEEE, 2010, 773-777.
3. A. Yona, T. Senjyu, and T. Funabashi, Application of recurrent neural network to short-term - ahead generating power forecasting for photovoltaic system, Power engineering society general meeting IEEE, 2007, 1-6.
4. S. A Kalogirou, Applications of artificial neural networks in energy systems: a review, Energy conversion management, 40(10), 1999, 1073-1087.
5. J. Catalao, H. Pousinho, and V. Mendes, Hybrid Wavelet-PSO-ANFIS approach for short-term electricity prices forecasting, Power Systems, IEEE Transactions on, 26 (1), 2011, 137-144.
6. A. Conejo, M. Plazas, R. Espinola, and A. Molina, Day-ahead electricity price forecasting using the wavelet transform and ARIMA models, Power Systems, IEEE Transactions on, 20(2),2005, 1035-1042.
7. S. Mallat, A theory for multi-resolution signal decomposition: the wavelet representation, Pattern Analysis and Machine Intelligence, IEEE Transactions on, 11(7), 1989, 674-693.
8. Y. Zhangang, C. Yanbo, and K. Cheng, Genetic algorithm-based RBF neural network load forecasting model, in: Power Engineering Society General Meeting. IEEE, 2007, 1-6.
9. Z. Bashir and M. El-Hawary, Applying wavelets to short-term load forecasting using PSO-based neural networks, Power Systems, IEEE Transactions on 24 (1) (2009) 20-27.
10. (2012, March) University of Oregon solar radiation monitoring laboratory website. [Online]. Available: http://solardat.uoregon.edu/
11. (2012, March) Weather data website. [Online]. Available: http://wunderground.com/