Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model

Renxi Gong (), Zhihuan Wei, Yan Qin, Tao Liu and Jiawei Xu
Additional contact information
Renxi Gong: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Zhihuan Wei: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Yan Qin: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Tao Liu: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Jiawei Xu: School of Electrical Engineering, Guangxi University, Nanning 530004, China

Energies, 2024, vol. 17, issue 18, 1-24

Abstract: Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R 2 ) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting.

Keywords: short-term electrical load forecasting; improved dung beetle optimization algorithm; parallel temporal convolutional network; gated recurrent unit (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/18/4667/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/18/4667/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:18:p:4667-:d:1481217

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().