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首页> 外文期刊>Applied Soft Computing >Optimal training subset in a support vector regression electric load forecasting model
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Optimal training subset in a support vector regression electric load forecasting model

机译：支持向量回归电负荷预测模型中的最优训练子集

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摘要

This paper presents an optimal training subset for support vector regression (SVR) under deregulated power, which has a distinct advantage over SVR based on the full training set, since it solves the problem of large sample memory complexity O(N~2) and prevents over-fitting during unbalanced data regression. To compute the proposed optimal training subset, an approximation convexity optimization framework is constructed through coupling a penalty term for the size of the optimal training subset to the mean absolute percentage error (MAPE) for the full training set prediction. Furthermore, a special method for finding the approximate solution of the optimization goal function is introduced, which enables us to extract maximum information from the full training set and increases the overall prediction accuracy. The applicability and superiority of the presented algorithm are shown by the half-hourly electric load data (48 data points per day) experiments in New South Wales under three different sample sizes. Especially, the benefit of the developed methods for large data sets is demonstrated by the significantly less CPU running time.
机译：本文提出了一种在失调功率下用于支持向量回归（SVR）的最优训练子集，与基于完整训练集的SVR相比，它具有明显的优势，因为它解决了样本存储器复杂度O（N〜2）大的问题，并且可以防止在不平衡的数据回归过程中过度拟合。为了计算建议的最佳训练子集，通过将最佳训练子集大小的惩罚项与完整训练集预测的平均绝对百分比误差（MAPE）耦合，构造了近似凸优化框架。此外，介绍了一种用于找到优化目标函数的近似解的特殊方法，该方法使我们能够从完整的训练集中提取最大信息，并提高整体预测精度。通过在三个不同样本量下在新南威尔士州进行的半小时电负荷数据（每天48个数据点）实验，表明了所提出算法的适用性和优越性。尤其是，通过显着减少CPU运行时间，可以证明针对大型数据集开发的方法的优势。

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