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

<p>This paper firstly analyzes the urban residential electricity load characteristics and extracts residential electricity load data through a non-intrusive electricity load monitoring framework with electricity load characteristics. Secondly, the gray Verhulst model is improved by using function transformation and residual correction to further improve its prediction accuracy. Finally, a prediction example analysis is carried out for the electric load under urban residential electricity security. The results show that the maximum prediction error of the improved gray Verhulst model is 2.28%, which is 1.34 percentage points lower than the 3.62% of the genetic algorithm GM(1,1) model. This indicates that the prediction of urban residential electricity security can be achieved using the improved gray Verhulst model.</p>
</abstract>

This paper firstly analyzes the urban residential electricity load characteristics and extracts residential electricity load data through a non-intrusive electricity load monitoring framework with electricity load characteristics. Secondly, the gray Verhulst model is improved by using function transformation and residual correction to further improve its prediction accuracy. Finally, a prediction example analysis is carried out for the electric load under urban residential electricity security. The results show that the maximum prediction error of the improved gray Verhulst model is 2.28%, which is 1.34 percentage points lower than the 3.62% of the genetic algorithm GM(1,1) model. This indicates that the prediction of urban residential electricity security can be achieved using the improved gray Verhulst model.

Prediction of urban residential electricity security based on Verhulst grey model

Applied Mathematics and Nonlinear Sciences

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

This paper firstly analyzes the urban residential electricity load characteristics and extracts residential electricity load data through a non-intrusive...

Prediction of urban residential electricity security based on Verhulst grey model

Online veröffentlicht: 18. Sept. 2023

Seitenbereich: -

Eingereicht: 03. Dez. 2022

Akzeptiert: 29. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00692

Schlüsselwörter
Electric load monitoring, Load characteristics, Residual correction, Gray Verhulst model

© 2023 Zhenjun Lu et al., published by Sciendo

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

Prediction of urban residential electricity security based on Verhulst grey model

Online veröffentlicht: 18. Sept. 2023

Seitenbereich: -

Eingereicht: 03. Dez. 2022

Akzeptiert: 29. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00692

SchlüsselwörterElectric load monitoring, Load characteristics, Residual correction, Gray Verhulst model

© 2023 Zhenjun Lu et al., published by Sciendo

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

Schlüsselwörter
Electric load monitoring, Load characteristics, Residual correction, Gray Verhulst model