Short-term wind speed prediction method based on CEEMD-VMD-GA-ORELM model

Abstract

The present invention relates to the technical field of electrical engineering, and more particularly, to a method based on CEEMD-VMD-GA. The short-term wind speed prediction method based on ORELM model firstly obtains the historical wind speed data and pretreats the data, then decomposes the historical wind speed data into a series of discrete modes with specific sparse attributes by using the complementary empirical mode decomposition and variational mode decomposition. Then genetic algorithm is used to optimize the outlier robust limit learning machine prediction model to predict all the subsequences in one step. Finally, all the predicted values of the sub-sequences are superposed and the actual predicted results are obtained. The invention utilizes two-layer decomposition of complementary empirical mode decomposition and variational mode decomposition to reduce non-stationarity and non-linearity of wind speed series, Genetic algorithm is used to optimize the outlier robust limit learning machine to form a hybrid model for single-step prediction, which reduces the influence of complex characteristics of wind speed series on prediction results, improves the accuracy of short-term wind speed prediction, and solves the problem of local optimization of neural network.

Description

technical field [0001] The present invention relates to the technical field of electrical engineering, more specifically, to a short-term wind speed prediction method based on the CEEMD-VMD-GA-ORELM model. Background technique [0002] As a renewable and clean energy, wind power has been developed on a large scale in my country in recent years. At the same time, the randomness, intermittency and volatility of wind power generation have brought security risks to the stability of the power grid and economic operation. Accurate wind speed prediction results can provide a strong basis for planning and scheduling, and have become an important part of the energy management system after large-scale wind power grid integration. [0003] At present, wind speed prediction can be divided into short-term, medium-term and long-term prediction, and their engineering significance is also different. Short-term wind speed prediction is an important basis for economic dispatch of power syste...

AI Technical Summary

Problems solved by technology

Compared with the conventional neural network model, the neural network model optimized by genetic algorithm (GA) makes up for many deficiencies, it avoids the defect that the parameters of the neural network fall into local optimum, and improves the generalization ability of the neural network, so It can be used for short-term wind speed prediction. However, due to the complex characteristics of non-stationary and nonlinear wind speed series, a single prediction model is easy to fall into the problem of local optimum on the one hand, and it is difficult to deal with the high nonlinearity of wind speed on the prediction results. influence, resulting in inaccurate prediction of wind speed

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