Short-term wind power prediction method based on EWT-PDBN combination
A technology of wind power forecasting and wind power, which is applied in forecasting, wind power generation, neural learning methods, etc., and can solve problems such as errors and low prediction accuracy
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[0094] Such as figure 1 As shown, the short-term wind power prediction method based on EWT-PDBN combination of the present invention comprises the following steps:
[0095] A. Collect the numerical weather forecast data and historical wind power data of the wind field. The numerical weather forecast data includes five sets of data including wind speed, temperature, air pressure, atmospheric pressure at mean sea level and relative humidity. The historical wind power data includes the historical maximum wind power data. Three sets of historical minimum wind power data and historical average wind power data;
[0096] B. Preprocess all the collected data, and then normalize all the preprocessed data;
[0097] C. Utilize the empirical wavelet transform (EWT) signal decomposition technology to decompose the normalized historical average wind power data, perform smoothing processing, and obtain multiple groups of subsequences with different characteristic frequencies;
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