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

<p>In order to solve the problem of the traditional gray prediction model (GM) during determination of the accuracy of buildings’ energy savings and its poor fitting of data, the idea of a fractional model based on the traditional first-order one-variable GM(1,1) model is applied. We use the GM–backpropagation (GM-BP) neural network to solve the optimal fractional order and establish a fractional GM(1,1) model based on the GM-BP neural network. Example calculation shows that the fractional GM(1,1) model can improve the prediction accuracy of buildings’ energy savings, and selecting the optimal order can further improve the prediction accuracy and decrease the error level when using the GM-BP neural network. This work shows that the fractional GM(1,1) model based on the GM-BP neural network has an important guiding role in the energy savings of buildings.</p>
</abstract>

In order to solve the problem of the traditional gray prediction model (GM) during determination of the accuracy of buildings’ energy savings and its poor fitting of data, the idea of a fractional model based on the traditional first-order one-variable GM(1,1) model is applied. We use the GM–backpropagation (GM-BP) neural network to solve the optimal fractional order and establish a fractional GM(1,1) model based on the GM-BP neural network. Example calculation shows that the fractional GM(1,1) model can improve the prediction accuracy of buildings’ energy savings, and selecting the optimal order can further improve the prediction accuracy and decrease the error level when using the GM-BP neural network. This work shows that the fractional GM(1,1) model based on the GM-BP neural network has an important guiding role in the energy savings of buildings.

Energy-saving technology of BIM green buildings using fractional differential equation

Chongqing Vocational Institute of Engineering

Department of Information Technology, Faculty of Computing and Information Technology

Department of Accounting and Finance, Faculty of Administrative Sciences

Applied Mathematics and Nonlinear Sciences

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

In order to solve the problem of the traditional gray prediction model (GM) during determination of the accuracy of buildings’ energy savings and its poor...

Energy-saving technology of BIM green buildings using fractional differential equation

Online veröffentlicht: 13. Dez. 2021

Seitenbereich: 481 - 490

Eingereicht: 17. Juni 2021

Akzeptiert: 24. Sept. 2021

DOI: https://doi.org/10.2478/amns.2021.2.00085

Schlüsselwörter
buildings’ energy savings prediction, GM(1,1) model, fractional GM(1,1) model

© 2021 Ya Qin et al., published by Sciendo

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

Energy-saving technology of BIM green buildings using fractional differential equation

Online veröffentlicht: 13. Dez. 2021

Seitenbereich: 481 - 490

Eingereicht: 17. Juni 2021

Akzeptiert: 24. Sept. 2021

DOI: https://doi.org/10.2478/amns.2021.2.00085

Schlüsselwörterbuildings’ energy savings prediction, GM(1,1) model, fractional GM(1,1) model

© 2021 Ya Qin et al., published by Sciendo

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

Schlüsselwörter
buildings’ energy savings prediction, GM(1,1) model, fractional GM(1,1) model