Wind power prediction correction method and system based on support vector machine

Abstract

The invention discloses a wind power prediction correction method. The method includes the steps that 1, the total capacity of a selected wind power plant is obtained, and wind power prediction data and wind power actual measurement data of the whole field in a recent civil year of the wind power plant are obtained; 2, normalization processing is carried out on the wind power data, obtained from the step1, of the wind power plant by means of the total capacity of the wind power plant; 3, an input and output data set is formed according to the wind power prediction data and the wind power actual measurement data obtained after preprocessing in the step 2; 4, 2 / 3 of the input and output data set obtained in the step 3 is selected randomly to serve as a training set, and the remaining 1 / 3 serves as a testing set; 5, a kernel function and training parameters of the support vector machine are selected, training is carried out by means of the training set obtained from the step 4, and the testing set is used for testing; 6, a grid searching method is utilized to correct the parameters of the support vector machine, and an average absolute percentage error and a root-mean-square relative error of a correction result are utilized to serve as evaluation criteria to obtain a local optimum support vector machine training model, namely a local optimum wind power prediction correction model.

Description

technical field [0001] The invention relates to the field of new energy power generation and control, in particular to a wind power prediction and correction method and system based on a support vector machine (SVM). Background technique [0002] Since the beginning of the new century, the shortage of fossil energy and environmental pollution have become more and more serious, prompting the power industry to seek and develop renewable clean energy to replace existing chemical energy and optimize the energy structure. Renewable clean energy has gradually attracted people's attention. On the other hand, wind energy, the primary energy source of wind power, has great volatility and intermittency, which will cause great interference to the power system. Therefore, it is necessary to predict the wind power and incorporate wind power into the conventional power generation plan in order to better Manage and utilize wind power. According to the requirements of the National Energy ...

AI Technical Summary

Problems solved by technology

However, in practice, the day-ahead forecast data reported by wind farms has a large error and cannot be used for the calculation of the day-ahead power generation plan, otherwise the day-ahead power generation plan will be very inaccurate and may even cause safety problems

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