An Attention-Driven Hybrid Deep Network for Short-Term Electricity Load Forecasting in Smart Grid

Jinxing Wang, Sihui Xue, Liang Lin (), Benying Tan and Huakun Huang ()
Additional contact information
Jinxing Wang: China State Grid Beijing Electric Power Company Daxing Power Supply Company, Beijing 102600, China
Sihui Xue: The School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Liang Lin: The Department of Information Engineering, Luoding Polytechnic, Yunfu 527200, China
Benying Tan: Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology), Guilin 541004, China
Huakun Huang: The School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2025, vol. 13, issue 19, 1-16

Abstract: With the large-scale development of smart grids and the integration of renewable energy, the operational complexity and load volatility of power systems have increased significantly, placing higher demands on the accuracy and timeliness of electricity load forecasting. However, existing methods struggle to capture the nonlinear and volatile characteristics of load sequences, often exhibiting insufficient fitting and poor generalization in peak and abrupt change scenarios. To address these challenges, this paper proposes a deep learning model named CGA-LoadNet, which integrates a one-dimensional convolutional neural network (1D-CNN), gated recurrent units (GRUs), and a self-attention mechanism. The model is capable of simultaneously extracting local temporal features and long-term dependencies. To validate its effectiveness, we conducted experiments on a publicly available electricity load dataset. The experimental results demonstrate that CGA-LoadNet significantly outperforms baseline models, achieving the best performance on key metrics with an R 2 of 0.993, RMSE of 18.44, MAE of 13.94, and MAPE of 1.72, thereby confirming the effectiveness and practical potential of its architectural design. Overall, CGA-LoadNet more accurately fits actual load curves, particularly in complex regions, such as load peaks and abrupt changes, providing an efficient and robust solution for short-term load forecasting in smart grid scenarios.

Keywords: smart grid; electric load forecasting; time-series; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/19/3091/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/19/3091/ (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:jmathe:v:13:y:2025:i:19:p:3091-:d:1758662

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

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