Multi-step temperature prediction model based on surrounding cities and long-term memory neural networks

被引：0

作者：

Huang, Zhenyue ^{[1
]}

Zhang, Lei ^{[1
]}

Shen, Xiajiong ^{[1
]}

Hou, Liyang ^{[1
]}

Gao, Yihua ^{[1
]}

Sun, Jun ^{[1
]}

机构：

[1] Henan Univ, Sch Comp & Informat Engn, Kaifeng, Peoples R China

来源：

14TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND EDUCATION (ICCSE 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

surrounding cities; long-term memory neural network; multi-step temperature prediction; weather;

D O I：

10.1109/iccse.2019.8845434

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, a multi-step temperature prediction model based on geographic location and long-term memory neural network is proposed. In order to verify the validity of the data of surrounding cities, two sets of comparative experiments are carried out which using 80-dimensional data of weather data added to surrounding cities and not joining the surrounding area. Urban weather data 8D data, 10D data added to surrounding city temperature data, and 1D data without temperature of surrounding cities. The test results show that the model of adding weather data of surrounding cities in most cases has better prediction effect than the model of weather data without surrounding cities. The results show that the model of weather data added to surrounding cities has better prediction effect.

引用

页码：518 / 522

页数：5

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