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The Application of Support Vector Machine in Load Forecasting

机译：支持向量机在负荷预测中的应用

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—The forecasting to mid-long term load is important because it can provide important evidence to the power planning. Traditional forecast techniques apply a single forecaster to carry out the task. However, this forecaster might not be the best for all situations or databases. A combinational model on the basis of Support Vector Machine (SVM) theory is proposed in this paper. During the process of the forecast, several single forecasting methods such as trend prediction model, exponent model, non-linear regression model, improved grey predictive model and improved grey verhulst predictive model, are used to form a model group, and then the fitted results by different traditional predictive models in time sequence act as the input of the support vector machine regression (SVMR) model, then by relative SVMR approach based on known input and output samples, we can obtain the test model. In the paper, the procedure of the combinational prediction on transformer faults based on SVMR is discussed in details. The example on load data has proven that the proposed model can give good results on both the fitting to the known data in time sequence and the extrapolation to the data to be predicted. Moreover, compared with other predictive approaches, both single model and other combinational model, the proposed combinational forecasting model has higher prediction accuracy.
机译：- 到中长期负荷的预测很重要，因为它可以为电力规划提供重要证据。传统的预测技术应用单个预测器来执行任务。但是，这项预测器可能对所有情况或数据库都不是最好的。本文提出了基于支持向量机（SVM）理论的组合模型。在预测过程中，几种单一预测方法，如趋势预测模型，指数模型，非线性回归模型，改进的灰度预测模型和改进的灰色verhulst预测模型，用于形成模型组，然后是拟合结果通过不同的传统预测模型在时间序列中充当支持向量机回归（SVMR）模型的输入，然后通过相对SVMR方法基于已知的输入和输出样本，我们可以获得测试模型。在本文中，详细讨论了基于SVMR的变压器故障的组合预测程序。加载数据的示例已经证明，所提出的模型可以在时间序列中对已知数据的拟合和预设的外推到要预测的数据。此外，与其他预测方法相比，单一模型和其他组合模型，所述组合预测模型具有更高的预测精度。

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