<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This article applies multiple nonlinear regression methods to establish a forecasting model for the load characteristics of air conditioning in shopping malls at different times. Based on Python data, determine the functional relationship of refrigerant parameters concerning pressure and temperature. The article uses kernel smoothing estimation technology to calculate the room temperature probability density distribution of users participating in DLC to characterize the user’s comfort. The article’s research results show that the average error between the regression analysis results of refrigerant parameters and the reference value is within 1%. This model is suitable for medium and long-term load forecasting. It has high prediction accuracy for the sudden change trend with a turning point.</p>
</abstract>

This article applies multiple nonlinear regression methods to establish a forecasting model for the load characteristics of air conditioning in shopping malls at different times. Based on Python data, determine the functional relationship of refrigerant parameters concerning pressure and temperature. The article uses kernel smoothing estimation technology to calculate the room temperature probability density distribution of users participating in DLC to characterize the user’s comfort. The article’s research results show that the average error between the regression analysis results of refrigerant parameters and the reference value is within 1%. This model is suitable for medium and long-term load forecasting. It has high prediction accuracy for the sudden change trend with a turning point.

Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Shijiazhuang University of Applied Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This article applies multiple nonlinear regression methods to establish a forecasting model for the load characteristics of air conditioning in shopping malls...

Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 499 - 506

Eingereicht: 13. Jan. 2022

Akzeptiert: 19. März 2022

DOI: https://doi.org/10.2478/amns.2022.2.0034

Schlüsselwörter
Load forecasting, Multiple nonlinear regression method, Regression index, Mall air conditioning

© 2023 Lihui Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 499 - 506

Eingereicht: 13. Jan. 2022

Akzeptiert: 19. März 2022

DOI: https://doi.org/10.2478/amns.2022.2.0034

SchlüsselwörterLoad forecasting, Multiple nonlinear regression method, Regression index, Mall air conditioning

© 2023 Lihui Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Schlüsselwörter
Load forecasting, Multiple nonlinear regression method, Regression index, Mall air conditioning