Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (9): 1256-1261.doi: 10.16183/j.cnki.jsjtu.2021.192

• Mechanical Engineering • Previous Articles     Next Articles

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. 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


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: