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.

Figures/Tables 10

Fig.1

Fig.2

Tab.1

Fig.3

Tab.2

Tab.3

Tab.4

Fig.4

Fig.5

Fig.6

References 14

[1]	中国建筑节能协会. 中国建筑能耗研究报告2020[J]. 建筑节能, 2021, 49(2): 1-6.
	China Association of Building Energy Efficiency. China building energy consumption annual report 2020[J]. Building Energy Efficiency, 2021, 49(2): 1-6.
[2]	FOUCQUIER A, ROBERT S, SUARD F, et al. State of the art in building modelling and energy performances prediction: A review[J]. Renewable and Sustainable Energy Reviews, 2013, 23: 272-288. doi: 10.1016/j.rser.2013.03.004 URL
[3]	GASSAR A A A, CHA S H. Energy prediction techniques for large-scale buildings towards a sustainable built environment: A review[J]. Energy and Buildings, 2020, 224: 110238.
[4]	FU X, ZENG X J, FENG P P, et al. Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China[J]. Energy, 2018, 165: 76-89. doi: 10.1016/j.energy.2018.09.156 URL
[5]	KIM J H, SEONG N C, CHOI W. Forecasting the energy consumption of an actual air handling unit and absorption chiller using ANN models[J]. Energies, 2020, 13(17): 4361.
[6]	ROBINSON C, DILKINA B, HUBBS J, et al. Machine learning approaches for estimating commercial building energy consumption[J]. Applied Energy, 2017, 208: 889-904. doi: 10.1016/j.apenergy.2017.09.060 URL
[7]	RAJAGUKGUK R A, RAMADHAN R A A, LEE H J. A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power[J]. Energies, 2020, 13(24): 6623. doi: 10.3390/en13246623 URL
[8]	FAN C, SUN Y J, ZHAO Y, et al. Deep learning-based feature engineering methods for improved building energy prediction[J]. Applied Energy, 2019, 240: 35-45. doi: 10.1016/j.apenergy.2019.02.052 URL
[9]	廖文强, 王江宇, 陈焕新, 等. 基于深度学习的建筑能耗短期预测方法研究[C]// 2019年中国家用电器技术大会论文集. 佛山: 电器杂志社, 2019: 1173-1178
	LIAO Wenqiang, WANG Jiangyu, Chen Huanxin, et al. Research on short-term building energy consumption prediction method based on deep-learning[C]// Proceeding of 2019 China Household Electrical Appliances Technical Conference. Foshan, China: Appliance, 2019: 1173-1178.
[10]	邹锋, 田大伟, 王悦, 等. 基于生成对抗网络的深度学习能耗预测算法[J]. 电脑知识与技术, 2019, 15(2): 198-200.
	ZOU Feng, TIAN Dawei, WANG Yue, et al. Deep learning energy consumption prediction algorithms based on generative adversarial networks[J]. Computer Knowledge and Technology, 2019, 15(2): 198-200.
[11]	RUNGE J, ZMEUREANU R. A review of deep learning techniques for forecasting energy use in buildings[J]. Energies, 2021, 14(3): 608. doi: 10.3390/en14030608 URL
[12]	YAMASHITA R, NISHIO M, DO R K G, et al. Convolutional neural networks: An overview and application in radiology[J]. Insights into Imaging, 2018, 9(4): 611-629. doi: 10.1007/s13244-018-0639-9 pmid: 29934920
[13]	HAZRA A, CHOUDHARY P, SHEETAL S M. Recent advances in deep learning techniques and its applications: An overview[C]// Advances in Biomedical Engineering and Technology. Singapore: Springer, 2020: 103-122.
[14]	Iowa State University. Iowa environmental mesonet[DB/OL]. (2019-10-22) [2021-03-16]. http://mesonet.agron.iastate.edu/request/download.phtml?network=CN__ASOS#.

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

[1]	Dong Ruyi, Shi Cong. Traffic Light Recognition Based on Improved YOLOv5l [J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 319-333.
[2]	GUO Qi, YAN Jun, HAO Qianpeng, HAN Dong, YANG Zhihao, YAN Xinyue, ZHANG Haipeng, LI Ran. Short-Term Wind Power Prediction Method Based on Closed-Loop Clustering and Multi-Objective Optimization [J]. Journal of Shanghai Jiao Tong University, 2026, 60(2): 246-255.
[3]	CHEN Liangwen, ZHU Yuxin, SHEN Tao, YU Yifan, LING Xiao, SHENG Qinghong. Deep Learning-Based Infrared Ship Target Wake Matching and Detection Algorithm [J]. Air & Space Defense, 2026, 9(1): 80-90.
[4]	LUO Zhijun, WANG Jianrui, YIN Jiawei. A Survey of Task-Driven Intelligent Target Recognition Methods in Complex Battlefield Environments [J]. Air & Space Defense, 2026, 9(1): 1-11.
[5]	RONG Guang, ZHANG Yexin, TANG Chao, CHEN Jinbao, ZHOU Yiling, WANG Jianyuan. Study on Simulation Data-Driven Fault Diagnosis Technology for Unmanned Aerial Vehicles [J]. Air & Space Defense, 2025, 8(6): 73-84.
[6]	CHEN Cheng, PENG Pan, TAO Wei, ZHAO Hui. Hyperspectral Satellite Image Classification Based on Feature Pyramid Networks With 3D Convolution [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1073-1084.
[7]	TAHIR Rizwana, CAI Yunze. Multi-Human Pose Estimation by Deep Learning-Based Sequential Approach for Human Keypoint Position and Human Body Detection [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1103-1113.
[8]	XIA Yilin, LIU Gang, YAN Congqiang, CAI Yunze. Research on Deep Learning-Based Rotation Detection Algorithms for Ship Wakes in SAR Images [J]. Air & Space Defense, 2025, 8(5): 64-74.
[9]	XU Qiang, MA Yuehua, XU Ke, PAN Jun. A Review on Intelligent Radar Target Recognition Methods [J]. Air & Space Defense, 2025, 8(5): 1-9.
[10]	PAN Meiqi, HE Xing. A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning [J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 561-568.
[11]	MA Changxi, HUANG Xiaoting, MENG Wei. Predicting Parking Spaces Using CEEMDAN and GRU [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 962-975.
[12]	YANG Zhuang, LI Zhaofei, WANG Jihua, WEI Xudong, ZHANG Yijie. Named Entity Identification of Chinese Poetry and Wine Culture Based on ALBERT [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 1065-1072.
[13]	JIANG Wenbo, ZHENG Hangbin, BAO Jinsong. Novel Multi-Step Deep Learning Approach for Detection of Complex Defects in Solar Cells [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 1050-1064.
[14]	LIANG Yuwan, XIAO Zhaoyun, LI Mingguang, MENG Jiangshan, ZHOU Jianfeng, HUANG Shanjing, ZHU Haojie. Ground Settlement Prediction by Vacuum Preloading Based on LSTM [J]. Journal of Shanghai Jiao Tong University, 2025, 59(4): 525-532.
[15]	ZHAO Ziyu, WANG Xuquan, MA Jie, XING Yujie, DUN Xiong, WANG Zhanshan, CHENG Xinbin. Edge Chip Deployment Methods for Lightweight Infrared Computational Imaging Reconstruction Algorithms [J]. Air & Space Defense, 2025, 8(4): 85-93.