Wind Power Prediction Model Based on ARMA and Improved BP-ANN

Hai Ke Liu; Jiang Xia Feng; Shen Quan Yang; Tao Jia

doi:10.4028/www.scientific.net/AMR.1008-1009.183

Paper Titles

Research the Influencing Factors of the Low Wind Speed Wind Turbine Fatigue Loads
p.164

The Distributed Intelligent Lubricating System for Wind Turbines
p.169

The Reserve Capacity Model Based on the Idea of Scenario in Power System
p.173

Use of the Savonius Wind Turbine for the Needs of Public Lighting
p.179

Wind Power Prediction Model Based on ARMA and Improved BP-ANN
p.183

Energy Efficiency Evaluation of Wind Energy Based on Energy Reports
p.188

Vibration and Damping Analysis of Carbon Fiber Wind Turbine Blade with Viscoelastic Damping Treatment
p.192

Study on Transient Stability of Circulation Economy Power System with High Penetration of Wind Power
p.197

Research of Fine and Robust State Estimation
p.202

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1008-1009Wind Power Prediction Model Based on ARMA and...

Wind Power Prediction Model Based on ARMA and Improved BP-ANN

Abstract:

In order to improve the prediction accuracy of wind power, this research is based on time series and improved BP-ANN algorithm. The basic idea is described as follows: wind speed forecasting model is established by using time series method; wind speed-wind power model is built by utilising improved BP-ANN algorithm; wind speed data from time series forecasting is used as input of neural network model, and the prediction results for wind power are obtained. In order to analyse the availability of wind power prediction model, the mean absolute error and correlation coefficient are compared to analyse the predictions results. The results show that the prediction model can effectively improve the forecasting accuracy of wind power.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1008-1009)

Pages:

183-187

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1008-1009.183

Citation:

Cite this paper

Online since:

August 2014

Authors:

Hai Ke Liu*, Jiang Xia Feng, Shen Quan Yang, Tao Jia

Keywords:

Improved Bp-ANN, Time Series, Wind Power Prediction

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] WANG Lijie， LIAO Xiaozhong，GAO Yang， et al: Power System Protection and Control， 2009，33（9）: 118-121.

Google Scholar

[2] GU Xingkai，FAN Gaofeng，WANG Xiaorong，et al: Power System Technology，2007，31（S2）：335-338.

Google Scholar

[3] ZHANG Yang，LI Qiang: Water Conservancy & Electric Power Machinery， 2007，29（7）：53-55.

Google Scholar

[4] JAMES W，TAYLOR P E，MCSHARRY: IEEE Transactions on Energy Conversion， 2009， 24(3).

Google Scholar

[5] LIANG Wu，JEONJIE P，JAESOK C，et al．A study on wind speed prediction using artificial neural network at Jeju Island in Korea [C]/ Transmission & Distribution Conference & Exposition，Seoul Korea: IEEE, 2009：1-4.

DOI: 10.1109/td-asia.2009.5356873

Google Scholar