Abstract Improving the precision of wind power forecasting can be helpful to the increase of dispatch efficiency.In this paper,based on the the investigation on the intrinsic volatility of wind power time series,the fat-tail generalized autoregressive conditional Heteroscedasticity (GARCH) in mean type short term wind power forecasting models are generalized.Based on different formulations of volatility compensation items,several types of the fat-tail GARCH-M models are derived.The proposed models can capture the direct relationship between the volatility of wind power time series and its conditional mean.Furthermore,the models can depict the fat-tail effect in the practical wind power time series with leptokurtosis feature to improve the forecasting performance.In the case study,by means of the historical coastal wind power data of Jiangsu wind farm,the parameters of the proposed models are estimated,the GARCH-M effect and the fat-tail effect in the wind power time series are verified,and the conditional mean and conditional variance of the wind power are forecasted.Case study results clearly illustrate the validation and effectiveness of the proposed methods.And it is clearified that the GARCH-M model with the consideration of fat-tail effect is effective to provide satisfying forecasting results.
Chen Hao,Wan Qiulan,Wang Yurong. Short-Term Wind Power Forecast Based on Fat-Tailed Generalized Autoregressive Conditional Heteroscedasticity-in-Mean Type Models[J]. Transactions of China Electrotechnical Society, 2016, 31(5): 91-98.
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2016, Vol. 31 
