Time series forecasting method of building energy consumption using support vector regression

被引：0
|
作者：
Liu, Dandan [1 ]
Chen, Qijun [2 ]
Mori, Kazuyuki [3 ]
机构：
[1] Shanghai Univ Elect Power, Sch Comp & Informat Engn, Shanghai 200090, CO, Peoples R China
[2] Tongji Univ, Sch Elect & Informat, Shanghai 201804, CO, Peoples R China
[3] Mitsubishi Electr Corp, Adv Technol R&D Ctr, Amagasaki, CO 6618661, Japan
来源：
2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION | 2015年
关键词：
energy consumption; building; support vector regression; time series analysis; NEURAL-NETWORKS; PREDICTION; IMPACT;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we focus on the prediction method of building energy consumption time series. The building energy consumption data can be regarded as a time series, which is usually nonlinear and non-stationary. Traditional time series analysis model has lower prediction accuracy. Then the machine learning method, especially support vector regression algorithm always has better performance to deal with non-stationary and nonlinear time series. So the support vector regression algorithm is applied to develop building energy consumption time series model. The model is applied in different buildings. Experimental results show the prediction accuracy of the model is better than traditional time series analysis model.
引用
收藏
页码：1628 / 1632
页数：5
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