Short-term wind power prediction method of CEEMD and random forest

Abstract

The invention discloses a short-term wind power prediction method based on complete ensemble empirical mode decomposition (CEEMD) and a random forest. The method comprises the following steps of 1) using a CEEMD technology to decompose an original wind power sequence into a series of intrinsic mode functions (IMFs) with different characteristics; 2) using an approximate entropy to calculate each intrinsic modal function complexity, merging modal functions with similar approximate entropy values into new components which are a random component, a detail component and a trend component; 3) carrying out zero equalization processing on different component data; 4) using a partial autocorrelation function (PACF) to determine an input variable set for the different components; and 5) constructing a random forest (RF) prediction model for each new component, superposing each component prediction result to acquire a final short-term wind power prediction value, and through an example, verifying validity of the method of the invention. By using the method of the invention, short-term wind power prediction precision is effectively increased and a short-term wind power prediction problem of an electric power system can be well solved.

Description

technical field [0001] The invention relates to a short-term wind power prediction method of an electric power system, which performs short-term prediction on the wind power of the electric power system and belongs to the technical field of electric power systems. Background technique [0002] As an important clean energy, wind energy has received extensive attention in the sustainable energy development strategy due to its abundant reserves and low power generation cost. With the increasing proportion of wind energy installed capacity year by year, it is urgent to solve the adverse effects of random uncertainty and fluctuation of wind power on the safe and stable operation of the power grid. Accurate wind power forecasting is an important means to ensure the balance between supply and demand of the power grid, and it is also an important reference for real-time power grid security analysis, automatic power generation control, and system backup arrangements. Therefore, impr...

AI Technical Summary

Problems solved by technology

However, in the process of estimating model parameters, SVM has the disadvantages that it is difficult to select the initial value of the parameters and the operation efficiency is low, and it is difficult to meet the analysis and processing of high-dimensional and large-sample data.

Images