Combination model for short-term load forecasting

被引：0
作者：
School of Information and Electromechanical Engineering, Shanghai Normal University, Shanghai, 0086/Shanghai, China [1 ]
机构：
[1] School of Information and Electromechanical Engineering, Shanghai Normal University, Shanghai
来源：
Chen, Q. (hellowangchenchen@163.com) | 1600年 / Bentham Science Publishers B.V., P.O. Box 294, Bussum, 1400 AG, Netherlands卷 / 05期
关键词：
Detrended data; Neural network; Regression; Short term load forecast; SVM;
D O I：
10.2174/1874444301305010124
中图分类号：
学科分类号：
摘要：
Gas demand possesses dual property of growing and seasonal fluctuation simultaneously, it makes gas demand variation possess complex nonlinear character. From previous studies know single model for nonlinear problem can't get good results but accurately gas forecast were essential part of an efficient gas system planning and operation. In recent years, lots of scholar put forward combination model to solve complex regression problem. In this paper, a new forecasting model which named regression combined neural network is presented. In this approach we used regression to model the trend and used neural network for calculating predicted values and errors. And to prove the effectiveness of the model, support vector machines(SVM) algorithm was used to compare with the result of combination model. The results show that the combination model is effective and highly accurate in the forecasting of short-term gas load and has advantage than other models. © Chen et al. Licensee Bentham Open.
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页码：124 / 132
页数：8
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