基于主成份分析法的神经网络电力负荷预测  

Neural Network Power Load Forecast Based on PCA-PSO-RBF

作　　者：胡亚超 刘超 陈勇 李美蓉 HU Ya-chao;LIU Chao;CHEN Yong;LI Mei-rong(State Grid Shandong Rlectric Power Company Zaozhuang Electric Power Company,Zaozhuang 277000 China)

机构地区：[1]国网山东省电力公司枣庄供电公司

出　　处：《自动化技术与应用》2019年第7期90-93,共4页Techniques of Automation and Applications

摘　　要：影响电力负荷的变量具多,其具有非线性程度高、冗余程度高等特点,传统方法预测结果精度不高。为了提高结果精度,利用主成份分析方法对人工神经网络进行优化,达到提高预测精度的目的。首先,利用粒子群算法优化、改进径向基函数神经网络。然后,对输入量进行主成份分析、筛选,把经分析、筛选的输入量重新输入神经网络。最后,进行训练、预测,得出结果。利用经过优化、改进的模型对某地级市2016年的电力负荷进行验证。结果表明,径向基函数神经网络经过粒子群算法的优化以及主成份的分析,负荷预测精度得到了提高。There are many variables influencing the power load, which have the characteristics of high degree of nonlinearity, high degree of redundancy, etc. The accuracy of traditional methods is not high. In order to improve the accuracy of results,principal component analysis methods are used to optimize the artificial neural network, to improve the prediction accuracy. First, the particle swarm optimization algorithm is used to optimize and improve the radial basis function neural network. Then, the principal component analysis and screening are performed on the input quantity, and the analyzed and filtered input quantity is re-inputted into the neural network. Finally, conduct training, forecasting, and get results. Use an optimized and improved model to verify the electricity load of a prefecture-level city in 2016. The results show that the radial basis function neural network is optimized by the particle swarm optimization and the analysis of the principal components, and the load forecasting accuracy is improved.

关 键 词：神经网络 电力负荷 粒子群 预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM712[自动化与计算机技术—控制科学与工程]