本文選題：用電負荷特征　切入點：支持向量回歸機　出處：《計算機工程》2017年09期

【摘要】：針對用電負荷的周期性特點,將用電負荷特征學習建模為小時、天數(shù)、負荷數(shù)3個維度的回歸問題,提出一種基于支持向量回歸機的三維回歸模型。將支持向量機的核函數(shù)設(shè)計為多個核函數(shù)的線性組合分別進行參數(shù)訓練,并給出多路徑逐步逼近的參數(shù)訓練算法。仿真結(jié)果表明,與三層神經(jīng)網(wǎng)絡(luò)、最小二乘非線性擬合模型相比,該模型具有較好的用電負荷特征學習與預測能力。
[Abstract]:According to the periodicity of electric load, the paper models the characteristic learning of power load as the regression problem of three dimensions: hour, day and load number. A three dimensional regression model based on support vector regression machine is proposed. The kernel function of support vector machine is designed as a linear combination of several kernel functions to train parameters separately. The simulation results show that compared with the three-layer neural network and the least square nonlinear fitting model, this model has a better ability to learn and predict the characteristics of power load.
【作者單位】： 長沙理工大學計算機與通信工程學院;
【基金】：國家自然科學基金(61303043) 湖南省自然科學基金(13JJ4052) 湖南省教育廳科學研究項目(13C1023)
【分類號】：O212.1;TM743

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