Short-term Load Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model

被引：0

作者：

Lu J. ^{[1
,2
]}

Zhang Q. ^{[1
]}

Yang Z. ^{[1
,2
]}

Tu M. ^{[1
,2
]}

Lu J. ^{[1
,2
]}

Peng H. ^{[1
,2
]}

机构：

[1] NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing

[2] State Key Laboratory of Smart Grid Protection and Control, Nanjing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 08期

关键词：

A hybrid model of CNN-LSTM network; Convolutional neural network (CNN); Long short-term memory (LSTM) network; Short-term load forecasting;

D O I：

10.7500/AEPS20181012004

中图分类号：

学科分类号：

摘要：

In order to screen out the effective information contained in massive data and improve the accuracy of short-term load forecasting, a hybrid model of short-term load forecasting method based on convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed due to the timing and nonlinear characteristics of load data, which takes massive historical load data, meteorological data, date information and peak-valley electricity price data as input by constructing a continuous feature map of time sliding window. Firstly, CNN is used to extract feature vectors. The feature vectors are constructed as time series and used as input data for LSTM network, which is utilized to forecast the short-term load. The proposed method is used to predict the power load data of an area in Jiangsu province. The experimental results show that the proposed prediction method has higher prediction accuracy than the traditional load forecasting method, the random forest forecasting method and the standard LSTM network forecasting method. © 2019 Automation of Electric Power Systems Press.

引用

页码：131 / 137

页数：6

共 27 条

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