摘要
Elman神经网络由于其具有无限逼近和适应时变特性的能力被广泛用于动态数据预测。短期的用电量存在多种不确定影响因素,为了将所有影响因素考虑其中,引入混沌时间序列的重构相空间技术。由于神经网络在非线性函数中对于峰值预测偏差较大,粗糙集理论可以对其做出修正。因此,引入混沌时间序列理论和粗糙集理论改进Elman神经网络并进行建模。模型应用嵌入维度和延迟时间重构相空间恢复原来系统的动力学形态,将处理好的数据代入Elman神经网络进行用电量预测。最后引入粗糙集修正误差较大的峰值点,提高预测精度。收集了Heriot-Watt大学某宿舍楼30天的用电量数据,以5 min为计数频率共8 640个计数点作为数据集进行预测仿真。预测结果与Elman神经网络和混沌时间序列Elman神经网络进行对比,验证了该模型在短时间预测的有效性。
Elman neural network is widely used for dynamic data prediction because of its ability to approximate and adapting to time-varying characteristics.There are many uncertain factors in the short-term electricity consumption.In order to take all the factors into account,this paper introduces the reconstruction phase space technology of chaotic time series.Due to the large deviation from the neural network of the peak prediction in nonlinear functions,it can be modified by rough set theory.Therefore,the chaotic time series theory and rough set theory are introduced to improve the Elman neural network.The model applies embedded dimension and delay time to reconstruct the phase space to restore the original system's dynamic morphology.The processed data is brought into the Elman neural network to predict the electricity consumption.Finally,the peak point corrected by the rough set is introduced to improve the prediction accuracy.This paper collects the data from a dormitory building in Heriot-Watt university of Edinburgh.It uses thirty days electricity data with 8 640 points as the data set to do predict simulation.The prediction results are compared with the Elman neural network and chaotic time series Elman neural network,and the validity of the model are verified in a short-time prediction.
作者
吴佳懋
李艳
符一健
WU Jiamao;LI Yan;FU Yijian(Hainan University,Haikou 570228,China;Heriot-Watt University,Edinburgh EH14 4AS,UK)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2019年第3期23-30,共8页
Power System Protection and Control
基金
国家自然科学基金项目资助(71572126)~~
关键词
混沌时间序列
ELMAN神经网络
粗糙集
用电量预测
chaotic time series
Elman neural network
rough set
prediction of electricity consumption
