An incremental electric load forecasting model based on support vector regression
An incremental electric load forecasting model based on support vector regression
复制标题
基于支持向量回归的增量电力负荷预测模型
DOI：
10.1016/j.energy.2016.07.092
复制
发表时间：
2016
期刊：
影响因子：
9
通讯作者：
Zhu SuLing
中科院分区：
文献类型：
--
作者：
Yang YouLong;Che JinXing;Li YanYing;Zhao YanJun;Zhu SuLing
随着智能便携式系统和网络数据库的日益增长，对批量到达和大样本数据集的学习要求越来越高。提出了一种增量式支持向量回归机（SVR）学习模型，用于批量到达、大样本情况下的电力负荷预测。对于支持向量回归建模，通过相空间重构（PSR）构造时间序列的最优嵌入。然后，基于当前数据集提取用于训练SVR的最优训练子集，这使得我们能够通过减少完整的训练数据集来降低高的时间和空间复杂度。当系统中加入新的数据时，提出了一种代表性数据集重构方法来快速重新训练当前的支持向量回归机，并提出了一种嵌套粒子群优化（NPSO）框架来选择增量式支持向量回归机模型的参数。增量电力负荷预测的实验表明，该模型的计算优于比较模型。
With the smart portable systems and the daily growth of databases on the web, there are ever-increasing requirements to learn the batch arriving and large sample data set. In this paper, an incremental learning model of support vector regression (SVR) is proposed to forecast the electric load under the batch arriving and large sample. For modeling with SVR, the optimal embedding of time series is constructed by phase space reconstruction (PSR). Then, an optimal training subset for the training of SVR is extracted based on the current data set, which enables us to cut the high time and space complexity by reducing the full training data set. When newly-increased data are added into the system, a representative data set reconstruction method is presented for quickly re-training the current SVR, and a nested particle swarm optimization (NPSO) framework is presented to select the parameters of the incremental SVR model. Experiments of incremental electric load forecasting demonstrate the computational superiority of the presented model over the comparison models.