A wind power forecasting method based on parallel wavelet neural network

Abstract

The invention belongs to the technical field of distributed energy generation control, in particular to a wind power generation power prediction method based on a parallel wavelet neural network, comprising the following steps: 1) acquiring condition parameters of a wind farm and wind power output data; 2) constructing a training data set and a test data set of a prediction sample; 3) constructingand training a parallel neural network model; 4) after completing the training of the parallel neural network model, putting into use. The invention shortens the training time of the neural network,not only retains the advantages of the traditional wavelet neural network in carrying out the time series prediction, but also solves the shortcomings that the parameters of the wavelet neural networkare difficult to be determined, and improves the prediction accuracy.

Description

technical field [0001] The invention belongs to the technical field of distributed energy generation control, and in particular relates to a method for predicting wind power generation power based on parallel wavelet neural networks. Background technique [0002] Wind power is a kind of green renewable energy. With the deterioration of the environment, wind energy has become the most attractive renewable energy. It has gradually attracted the attention of various countries and developed rapidly around the world. Short-term wind power forecasting plays a vital role in the planning and design of wind power system, grid scheduling and other issues. Due to the intermittent and random nature of wind energy, wind power generation has strong nonlinearity and instability, which is more difficult than short-term power load forecasting. How to quickly and accurately predict wind power generation power has become a research hotspot. [0003] Wind power generation prediction can provid...

AI Technical Summary

Problems solved by technology

However, since the weight, scaling factor, and time translation factor of the wavelet neural network will not only affect the prediction accuracy, but even affect whether the network can converge, how to obtain appropriate parameters has become the focus and difficulty of research.

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