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

Active Publication Date: 2020-09-22
SHIJIAZHUANG TIEDAO UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a short-term wind power prediction method based

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  • Short-term wind power prediction method based on EWT-PDBN combination
  • Short-term wind power prediction method based on EWT-PDBN combination
  • Short-term wind power prediction method based on EWT-PDBN combination

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Embodiment Construction

[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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Abstract

The invention provides an EWT-PDBN combination-based short-term wind power prediction method. The method comprises the following steps of A, collecting numerical weather forecast data and historical wind power data of a wind power plant; b, performing preprocessing and normalization processing of all the acquired data; c, decomposing the normalized historical average wind power data by using an empirical wavelet transform signal decomposition technology; d, performing correlation screening of the decomposed different intrinsic mode component function sub-sequences, respectively taking the screened group sub-sequences and other data subjected to normalization processing as input data, and inputting the input data into a particle swarm optimization deep belief network model for prediction toobtain group prediction data; and E, superposing a group of prediction data to reconstruct a group of data, and then performing reverse normalization processing of the group of data to obtain the result as the final wind power prediction result. The method is advantaged in that through EWT-PDBN combined prediction, the wind power prediction result with high precision and small error is obtained.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, in particular to a short-term wind power forecasting method based on EWT-PDBN combination. Background technique [0002] Compared with traditional energy sources, wind energy is one of the intermittent new energy sources with broad development prospects. However, due to continuous changes in the climate and environment, wind speed and wind power generation present characteristics such as volatility and randomness. In recent years, the installed capacity of wind power in my country has continued to grow. The instability of wind power has weakened the controllability of power systems containing wind power to electric energy, and at the same time brought many problems to the safe and stable operation of power systems after large-scale wind power grid integration. Restricted the further development of wind power. Although there are some methods to solve these problems at present, most ...

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Application Information

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IPC IPC(8): H02J3/38
CPCH02J3/381G06Q10/04G06N3/006G06N3/08H02J2300/28G06N3/045G06F18/214Y02E10/76Y02A30/00
Inventor 王硕禾张嘉姗郭威常宇健蔡承才刘晗
Owner SHIJIAZHUANG TIEDAO UNIV
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