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

doi:10.16183/j.cnki.jsjtu.2021.192

Abstract

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

CLC Number:

TU855

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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References 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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