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• W4205158889 abstract "Wind power point prediction can only give the exact predicted power value at a certain time point, and the fluctuation range of the predicted value cannot be obtained, which brings a lot of problems and troubles to wind power grid connection. In this paper, a short-term wind power interval prediction method based on Kalman filter and non-parametric kernel density estimation is proposed. First, the predicted value of wind power points is obtained by Kalman filter method; Second, the prediction error is statistically analyzed, and the probability density function of the error is obtained by kernel density estimation method; Finally, the fluctuation interval of wind power is predicted under the given confidence degree. Simulation analysis on the data of a wind farm in Oklahoma show at the proposed interval prediction method not only has higher accuracy but also has better general applicability." @default.
• W4205158889 created "2022-01-25" @default.
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• W4205158889 date "2021-07-18" @default.
• W4205158889 modified "2023-10-18" @default.
• W4205158889 title "Wind Power Prediction Based on Kalman Filter and Non-parametric Kernel Density Estimation" @default.
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• W4205158889 doi "https://doi.org/10.1109/icpsasia52756.2021.9621598" @default.
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