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Short-term Forecasting of Wind Power and Non-parametric Confidence Interval Estimation

ZHOU Songlin,MAO Meiqin,SU Jianhui(Research Center for Photovoltaic System Engineering Ministry of Education,Hefei University of Technology, Hefei 230009,Anhui Province,China)  
To meet the requirements of network planning,the forecasting system of wind power should provide exact forecasted value and make a reasonable assessment of risk which implied in forecasted values.Artificial neural network was applied to forecasting wind speed and wind direction,and wind power forecasting results were achieved according to the measured power curve.The uncertainty factors of the wind power forecasting were analyzed,and a non-parametric confidence interval estimation method was proposed based on analyzing the statistical characteristics of forecast errors.By means of the method,a probability density function model for forecasting errors in each power section was established,and the probabilistic forecasting results of wind power were obtained on the base of deterministic forecasting.The practicality and effectiveness of the proposed approach are verified by simulation results.
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