Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features

Very-short term wind power forecasting is one of the most effective ways to deal with the challenges of increased penetration of wind power into the electric grid due to its fluctuation and volatility. To improve wind power forecasting by taking advantage of each independent forecasting model, a hybrid model is proposed by means of grey relational analysis and wind speed distribution features. The weight of each independent model is estimated according to different wind speed subsection and similar wind speed frequency. The case study shows that the hybrid forecasting model has broader applications in very-short term (15-minute-ahead) wind power output forecasting.

[1]  George Stavrakakis,et al.  Wind power forecasting using advanced neural networks models , 1996 .

[2]  Yongqian Liu,et al.  Genetic algorithm-piecewise support vector machine model for short term wind power prediction , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[3]  John Adams,et al.  Impact of wind generation on system operations in the deregulated environment: ERCOT experience , 2009, 2009 IEEE Power & Energy Society General Meeting.

[4]  Zhang Huijuan,et al.  Grey Correlation in the Combined Weights of Power Load Forecasting Application , 2008, 2008 International Conference on Information Management, Innovation Management and Industrial Engineering.

[5]  Yongping Yang,et al.  Piecewise Support Vector Machine Model for Short-Term Wind-power Prediction , 2009 .

[6]  Z. Huang,et al.  Use of time-series analysis to model and forecast wind speed , 1995 .

[7]  M. Negnevitsky,et al.  Short term wind power forecasting using hybrid intelligent systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[8]  K. Agbossou,et al.  Nonlinear model identification of wind turbine with a neural network , 2004, IEEE Transactions on Energy Conversion.

[9]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .

[10]  Niu Dongxiao,et al.  Combining simulate anneal algorithm with support vector regression to forecast wind speed , 2010, 2010 Second IITA International Conference on Geoscience and Remote Sensing.

[11]  Feng Gao,et al.  Wind generation scheduling and coordination in ERCOT Nodal market , 2012, 2012 IEEE Power and Energy Society General Meeting.

[12]  Yongqian Liu,et al.  The research and application of wavelet-support vector machine on short-term wind power prediction , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[13]  David Maggio,et al.  Utilization of forecasts for Wind-powered Generation Resources in ERCOT operations , 2010, IEEE PES General Meeting.

[14]  Li Xue-ming.  Status Quo and Prospect on Control Technique for Large-scale Synchronization of Wind Power , 2011 .