Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System-A Case Study on Electrical Load Forecasting
Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System-A Case Study on Electrical Load Forecasting
复制标题
基于最优特征选择系统的混合预测模型研究与应用——以电力负荷预测为例
DOI：
10.3390/en10040490
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发表时间：
2017-04-01
期刊：
影响因子：
3.2
通讯作者：
Guo, Zhenhai
中科院分区：
文献类型：
--
作者：
Dong, Yunxuan;Wang, Jianzhou;Guo, Zhenhai
随着智能电网现代化进程的加快，电力系统调度和运行的复杂性和不确定性显著增加，而为了发展一个更加可靠、灵活、高效和具有弹性的电网，电力负荷预测不仅是一个重要的关键问题，而且仍然是一项困难和具有挑战性的任务。本文提出了一种以金字塔系统和递归神经网络为特征学习单元的短期电力负荷预测模型，该模型能有效地提高电网的稳定性和安全性。本文对9种特征学习方法进行了比较，选择了最好的一种作为学习目标，并用两个准则对预测区间的精度进行了评价。在此基础上，建立了基于递归神经网络的电力负荷预测方法，得到了历史数据的关系图，并将该方法应用于澳大利亚新南威尔士州的电力负荷预测。仿真结果表明，所提出的混合模型不仅能令人满意地逼近实际值，而且能够作为智能电网规划的有效工具。
The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.