Energy Consumption Prediction of Office Buildings Based on CNN-RNN Combined Model

被引：0
|
作者：
Zeng G. [1 ]
Wei Z. [1 ]
Yue B. [2 ]
Ding Y. [2 ]
Zheng C. [2 ]
Zhai X. [1 ]
机构：
[1] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai
[2] Guangdong Midea HVAC Equipment Co., Ltd., Guangdong, Foshan
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2022年 / 56卷 / 09期
关键词：
convolutional neural network (CNN); deep learning; prediction of building energy consumption; recurrent neural network(RNN);
D O I：
10.16183/j.cnki.jsjtu.2021.192
中图分类号：
学科分类号：
摘要：
In order to accurately reflect the operation characteristics of office buildings, a convolutional neural network(CNN)-recurrent neural network(RNN)combined model for energy consumption prediction of office buildings is proposed by using the good feature extraction ability of CNN and the good time series learning ability of RNN. Besides, a two-dimensional matrix data input structure suitable for the deep learning model is designed. The case study results show that compared with the simple recurrent neural network and long short term memory network, both the prediction accuracy and computational efficiency of CNN-RNN combined model are significantly improved, and the generalization of the model is also good. © 2022 Shanghai Jiao Tong University. All rights reserved.
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页码：1256 / 1261
页数：5
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