Short-Term PV Generation System Forecasting Model without Solar Radiation Based on Improved Wavelet Neural Network

Shuo Wang; Yong Hai Xu; Cai Jie Fan

doi:10.4028/www.scientific.net/AMM.291-294.83

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 291-294Short-Term PV Generation System Forecasting Model...

Short-Term PV Generation System Forecasting Model without Solar Radiation Based on Improved Wavelet Neural Network

Abstract:

The PV generation system is an uncontrollable source and has an affect on the grid by randomness of output power. So we need to strengthen the study of PV output power forecast. In this paper, according to the related information of recent day with the same weather type, an improved wavelet neural network forecasting model without solar radiation was proposed. Furthermore, with the measured data came from a PV power plant, comparison experiments were made as opposed to improved wavelet neural network forecasting model and the wavelet neural network forecasting model with traditional learning algorithm. The experimental results indicate that the improved wavelet neural network forecasting model can significantly improve the precision of PV output power prediction. The comparison experiments considering solar radiation were also given, which also show the high precision and high efficiency of proposed model and algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 291-294)

Pages:

83-88

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.291-294.83

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Online since:

February 2013

Authors:

Shuo Wang, Yong Hai Xu, Cai Jie Fan

Keywords:

BP Algorithm, Forecasting Model, Improved Learning Algorithm, Solar Radiation, Wavelet Neural Network (WNN)

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References

[1] Changsong Chen, Shanxu Duan, Jinjun Yin: Transactions of China Electromechanical Society. Vol. 24(2009), p.153(in Chinese).

Google Scholar

[2] DAI Qian, DUAN Shanxu, CAI Tao: Proceedings of the CSEE. Vol. 33 (2011), p. (in Chinese).

Google Scholar

[3] DING Ming, WANG Lei, BI Rui: Power System Protection and Control, Vol. 40(2012), p.93(in Chinese).

Google Scholar

[4] Linxing Chun, Caojia Cong, Chen Jie: Journal of Donghua University. vol. 33(2007), p.573(in Chinese).

Google Scholar

[5] Mellit A, Arab A H, Khorissi N. An ANFIS based forecasting for Solar radiation data from sunshine duration and ambient temperature. IEEE Power Engineering Society General Meeting (2007).

DOI: 10.1109/pes.2007.386131

Google Scholar