基于支持向量机的燃气短期负荷预测

Short-term Gas Load Forecast Based on Support Vector Machine

介绍支持向量机的原理和支持向量回归模型,提出支持向量回归(SVR)模型的城市燃气短期负荷预测方法。探讨输入样本数据的选择和预处理方法、核函数和支持向量机参数的选择,结合某城市燃气日负荷数据进行燃气短期负荷预测。与BP神经网络预测方法相比,支持向量回归模型预测方法用于小样本情况下的燃气短期负荷预测精度略高。

The principle of support vector ma- chine and the support vector regression model are intro- duced. A short-term city gas load forecast method based on support vector regression model is proposed. The selection and pretreatment method of input sample data and the selection of kernel function support vector machine parameters are discussed. The short-term gas load forecast is conducted with the daily gas load data of a city. For small sample data, the forecast method based on support vector regression model has higher short-term city gas load forecast precision than the fore- cast method based on BP neural network.