Short-term wind power prediction method based on integrated empirical mode decomposition and deep belief network

Abstract

The invention discloses a short-term wind power prediction method based on integrated empirical mode decomposition and a deep belief network. The short-term wind power prediction method comprises the steps of: decomposing an original wind power sequence into a series of intrinsic mode functions with different features by adopting integrated empirical mode decomposition, calculating sample entropy of the original wind power sequence and the intrinsic mode functions, combining the intrinsic mode functions with similar sample entropy values into a new sequence, and forming a random component, a detail component and a trend component; selecting an input variable set by adopting a partial autocorrelation function; constructing a training sample set according to the input variable set of each component; and establishing a deep belief network short-term wind power prediction model for each component, and superposing prediction results of the components, so as to obtain a final short-term wind power predicted value. The short-term wind power prediction method provided by the invention effectively improves the short-term wind power prediction precision, and can effectively solve the wind power prediction problem of the electric power system, so as to provide more reliable guarantee for large-scale wind power integration.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and in particular relates to a short-term wind power prediction method based on integrated empirical mode decomposition and deep belief network for short-term prediction of the wind power output of the electric power system. Background technique [0002] With the vigorous development and promotion of clean energy (such as wind energy, solar energy, etc.), the situation of energy shortage and environmental degradation has been alleviated to a certain extent, but its own volatility and randomness have brought great importance to the safe and stable operation of the power grid. challenge. In recent years, the penetration rate of wind energy in the power grid has increased year by year. In 2016, the installed capacity of wind power nationwide was 23.37 million kilowatts. By the end of 2016, the cumulative installed capacity of wind power nationwide was 169 million kilowatts. Accuratel...

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Problems solved by technology

[0004] Aiming at the problems existing in the existing short-term wind power prediction technology of power system, such as low prediction accuracy and difficulty in selecting model parameters, a short-term wind power prediction method based on integrated empirical mode decomposition and deep belief network is proposed. The belief network establishes a short-term wind power prediction model, which further improves the prediction performance compared with the traditional neural network prediction method; in order to effectively select the set of input variables that have a greater contribution to the load, the partial autocorrelation function is used to measure the correlation between the two variables In order to avoid the shortage of input variables selected by manual experience and improve engineering adaptability; in addition, in order to analyze the local internal variation law of wind power in detail, the present invention uses integrated empirical mode decomposition technology to decompose the original wind power sequence into a series of different characteristics The subsequence of the modal function, that is, the modal function, and then model and analyze each modal function, and use the partial autocorrelation function to select effective input variables according to its changing characteristics, which greatly improves the prediction accuracy

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