|Table of Contents|

Steam Prediction of Central Air Conditioning Based on Hybrid Deep-learning Model(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年03期
Page:
53-62
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Steam Prediction of Central Air Conditioning Based on Hybrid Deep-learning Model
Author(s):
Wang Ke1; Lu Huanda2; Zheng Junhong1; He Lili1
(1.School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China)(2.School of Computer and Data Engineering,NingboTech University,Ningbo 315100,China)
Keywords:
air conditioning steam consumption; convolutions over volumes; PredRNN++; GRU; spatial feature
PACS:
TP181
DOI:
10.3969/j.issn.1672-1292.2022.03.008
Abstract:
In order to solve the steam consumption prediction problem of several large central air-conditioning units,a hybrid deep-learning prediction model based on GRU and 3DConv-PredRNN++ is proposed. Aiming at the dynamic linkage relationship of multiple air conditioning units,the relationship between steam consumption of units is extracted by convolutions over volumes and PredRNN++method,which is used as a feature of spatial factors to participate in model prediction. In order to capture the overall trend and local variation of the steam consumption series, smooth process model,trend model and periodic model are used as model inputs in the data set. In order to improve the prediction performance of the model,a GRU is used to combine the external factor features and capture the temporal factor features. Finally,the model is constructed by parameter matrix fusion. Through comparison experiments with various prediction models,it is proved that the prediction accuracy of the model is superior and that it is necessary to extract spatial factors to participate in model prediction. Compared with the current model,the average standard energy consumption of this model is reduced by 60.09%.

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Last Update: 2022-09-15