基于CNN-RNN组合模型的办公建筑能耗预测
收稿日期: 2021-05-23
网络出版日期: 2022-10-09
Energy Consumption Prediction of Office Buildings Based on CNN-RNN Combined Model
Received date: 2021-05-23
Online published: 2022-10-09
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 . DOI: 10.16183/j.cnki.jsjtu.2021.192
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.
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