Gas load combination prediction method based on support vector regression

Abstract

The invention discloses a gas load combination prediction method based on support vector regression and relates to gas load prediction methods. According to the combination prediction method, a data preprocessing technology, an improved genetic algorithm and support vector regression are combined, and the method is mainly used for solving the problems that in the prior art, urban gas load prediction is low in precision and poor in applicability. The method comprises the steps that first, a correlation coefficient method is adopted to analyze the correlation between different influence factorsand gas loads, and singular spectrum analysis is adopted to perform de-noising processing on the obtained main influence factors; second, processed data is adopted to train a support vector regressionmodel, nuclear parameters and penalty factors are optimized in combination with the improved genetic algorithm, and finally a support vector regression model with an optimal training result is obtained; and last, the trained support vector regression model is utilized to predict gas load indexes in a future period of time. Through the combination prediction method, a short-term gas load prediction error can be substantially lowered, and prediction precision can be improved.

Description

technical field [0001] The invention relates to a gas load forecasting method, in particular to a gas load combination forecasting method based on support vector regression. Background technique [0002] In the natural gas supply system, gas load data is the basis and important basis for engineering design, scheduling management, operation control and pipeline operation optimization. At present, the methods used for gas load forecasting can be mainly divided into three categories: statistical methods, artificial intelligence algorithms and combined forecasting methods. Each of these prediction methods has certain defects, mainly in the following aspects: [0003] ① Statistical methods have limited applicability and low forecasting accuracy: forecasting methods based on statistical theory are often used as long-term gas load forecasting methods, but for gas load forecasting with a large amount of data, a large forecasting time range, and a short time step, the results Not i...

AI Technical Summary

Problems solved by technology

[0003] ① The scope of statistical methods is limited, and the prediction accuracy is not high: the prediction method based on statistical theory is often used as a long-term gas load prediction method, but for gas load prediction with a large amount of data, a large prediction time range, and a short time step, the results not ideal

Although different scholars at home and abroad have proposed a variety of different combination models, a unified and standardized combination forecasting model structure system has not yet been established.

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