基于CNN-RNN组合模型的办公建筑能耗预测

doi:10.16183/j.cnki.jsjtu.2021.192

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (9): 1256-1261.doi: 10.16183/j.cnki.jsjtu.2021.192

基于CNN-RNN组合模型的办公建筑能耗预测

曾国治¹, 魏子清¹, 岳宝², 丁云霄², 郑春元², 翟晓强¹()

1.上海交通大学机械与动力工程学院,上海 200240
2.广东美的暖通设备有限公司,广东佛山 528311

收稿日期:2021-05-23 出版日期:2022-09-28 发布日期:2022-10-09
通讯作者: 翟晓强 E-mail:xqzhai@sjtu.edu.cn
作者简介:曾国治(1998-),男,四川省泸州市人,硕士生,主要从事智慧建筑与建筑节能研究.

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

ZENG Guozhi¹, WEI Ziqing¹, YUE Bao², DING Yunxiao², ZHENG Chunyuan², ZHAI Xiaoqiang¹()

1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. Guangdong Midea HVAC Equipment Co., Ltd., Foshan 528311, Guangdong, China

Received:2021-05-23 Online:2022-09-28 Published:2022-10-09
Contact: ZHAI Xiaoqiang E-mail:xqzhai@sjtu.edu.cn

摘要/Abstract

摘要：

为准确反映办公建筑的运行特性,利用卷积神经网络(CNN)良好的特征提取能力与循环神经网络(RNN)良好的时序学习能力,提出用于预测办公建筑能耗的CNN-RNN组合模型,并对应设计了适用于深度学习模型的二维矩阵数据输入结构.案例分析结果表明,相较于简单循环神经网络和长短期记忆网络,CNN-RNN组合模型的预测精度与计算效率均显著提升,模型泛化性好.

关键词: 建筑能耗预测, 卷积神经网络, 循环神经网络, 深度学习

Abstract:

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.

Key words: prediction of building energy consumption, convolutional neural network (CNN), recurrent neural network(RNN), deep learning

中图分类号:

TU855

曾国治, 魏子清, 岳宝, 丁云霄, 郑春元, 翟晓强. 基于CNN-RNN组合模型的办公建筑能耗预测[J]. 上海交通大学学报, 2022, 56(9): 1256-1261.

ZENG Guozhi, WEI Ziqing, YUE Bao, DING Yunxiao, ZHENG Chunyuan, ZHAI Xiaoqiang. Energy Consumption Prediction of Office Buildings Based on CNN-RNN Combined Model[J]. Journal of Shanghai Jiao Tong University, 2022, 56(9): 1256-1261.

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参考文献 14

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网络层序号	类型	神经元个数	激活函数
1	Conv_1D	64	ReLU
2	Conv_1D	64	ReLU
3	SRNN(返回序列)	64	ELU
4	SRNN	32	ELU
5	Dense	32	ReLU
6	Dense	16	ReLU
7	Dense	8	ReLU

时间轴	模型输出能耗数据/ (kW·h)	模型输入
时间轴	模型输出能耗数据/ (kW·h)	室外气温/℃	相对湿度/%	工作日	时刻	历史序列
t+1	W_t₊₁	T_t₊₁	φ_t₊₁	D_t₊₁	H_t₊₁	W_t_-23
t		T_t	φ_t	D_t	H_t	W_t_-24
t-1		T_t_-1	φ_t_-1	D_t_-1	H_t_-1	W_t_-25
t-2		T_t_-2	φ_t_-2	D_t_-2	H_t_-2	W_t_-26
…		…	…	…	…	…
t-45		T_t_-45	φ_t_-45	D_t_-45	H_t_-45	W_t_-69
t-46		T_t_-46	φ_t_-46	D_t_-46	H_t_-46	W_t_-70
t-47		T_t_-47	φ_t_-47	D_t_-47	H_t_-47	W_t_-71

模型	可训练参数	训练时间/s
SRNN	17596	1193
LSTM	65089	1745
CNN-RNN	17633	848

模型	数据集	MAE/(kW·h)	MAPE/%	R²
SRNN	训练集	28.2	15.38	0.956
	测试集	31.3	15.93	0.946
LSTM	训练集	51.8	25.92	0.859
	测试集	56.2	26.54	0842
CNN-RNN	训练集	32.4	12.97	0.934
	测试集	36.1	13.73	0.918

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基于CNN-RNN组合模型的办公建筑能耗预测

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

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