Deep CNN & LSTM network for appliances energy forecasting in residential houses using IoT sensors

被引：5
|
作者：
Ploysuwan, Tuchsanai [1 ]
机构：
[1] Siam Univ, Dept Elect Engn, Bangkok, Thailand
来源：
2019 7TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON 2019) | 2019年
关键词：
Time Series; convolutional neural networks; long short-term memory; Energy Forecasting; PREDICTION;
D O I：
10.1109/ieecon45304.2019.8938914
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, the author has proposed the combination of deep convolutional neural networks as feature learning and long short-term memory as time series forecasting algorithm to forecast appliances energy consumption in residential houses using 28 data features collected from IoT sensors database. It is shown in experiment that the forecasting performance can be improved with comparison methods such as GBM (Gradient Boosting Machine), Support vector Machine with Radial Kernel, and RF (Random Forest).
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页数：4
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