Short-term load forecasting using a kernel-based support vector regression combination model
Short-term load forecasting using a kernel-based support vector regression combination model
复制标题
使用基于核的支持向量回归组合模型进行短期负荷预测
DOI：
10.1016/j.apenergy.2014.07.064
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发表时间：
2014-11-01
期刊：
影响因子：
11.2
通讯作者：
Wang, JianZhou
中科院分区：
文献类型：
--
作者：
Che, JinXing;Wang, JianZhou
基于核的支持向量回归（SVR）等方法在短期负荷预测（STLF）应用中表现出令人满意的性能。然而，基于核的方法的良好性能取决于选择适合学习目标的核函数，不合适的核函数或超参数设置可能导致性能明显下降。为了得到STLF问题的最优核函数，本文采用一种新颖的个体模型选择算法，提出了一种基于核的SVR组合模型。此外，该组合模型为支持向量回归模型的核函数选择提供了一种新的方法。利用澳大利亚和美国加州电网的实际数据，分别对所提模型的性能和负荷预测精度进行了评估。数值表和图的仿真结果表明，与最优的基于单个核的支持向量回归模型相比，所提出的组合模型提高了电力负荷预测的精度。(c) 2014 Elsevier Ltd.版权所有。
Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short-term load forecasting (STLF) application. However, the good performance of kernel-based method depends on the selection of an appropriate kernel function that fits the learning target, unsuitable kernel function or hyper-parameters setting may lead to significantly poor performance. To get the optimal kernel function of STLF problem, this paper proposes a kernel-based SVR combination model by using a novel individual model selection algorithm. Moreover, the proposed combination model provides a new way to kernel function selection of SVR model. The performance and electric load forecast accuracy of the proposed model are assessed by means of real data from the Australia and California Power Grid, respectively. The simulation results from numerical tables and figures show that the proposed combination model increases electric load forecasting accuracy compared to the best individual kernel-based SVR model. (c) 2014 Elsevier Ltd. All rights reserved.