|Table of Contents|

Forecasting Model of the Time Required for Central Air Conditioning to Achieve Control Effect Based on Divided Working Mode(PDF)

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

Issue:
2023年01期
Page:
56-65
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Forecasting Model of the Time Required for Central Air Conditioning to Achieve Control Effect Based on Divided Working Mode
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:
time required for central air conditioning to achieve control effect; combined prediction mode; random forest; depth feature extraction; over-fitting problem; sample unbalanced
PACS:
TP181
DOI:
10.3969/j.issn.1672-1292.2023.01.008
Abstract:
In order to solve the prediction problem of the time for central air conditioning to achieve the control effect, while meeting central air conditioning temperature-humidity control process requirements and enterprise energy saving reduction requirements, a combined prediction model is proposed in accordance with different working methods of air conditioning units.In the mode of Heating-Humidifying, the classification prediction model is constructed by Random Forest algorithm, and the advanced features extracted from depth features are used as the input of the model, which solves the over-fitting problem of small sample classification prediction.IPSO optimization parameters are used to reduce the time complexity of the algorithm. In the working mode of Cooling-Dehumidifying, the algorithm uses KNN to dynamically divide the category interval and uses density peak to improve SMOTE algorithm, so as to solve the problem of unbalanced sample. Then, the XGBoost algorithm is used to build the classification model. Considering that the air conditioning delay is turned on to generate a large loss to the workshop operation, a multi-angle comprehensive evaluation method is adopted to evaluate the model. Finally, the effectiveness and practicality of the combined model is verified by comparative experiments with a variety of predictive models. Experimental results show that the mean absolute error of the combined model is 3.2 minutes, and that the energy consumption of the combined model is 14.71% lower than that of the current model.

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Last Update: 2023-03-15