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Special Topics in Trend Forecasting Based on Artificial Intelligence
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
381-390

Short-term power load forecasting based on component-aware dynamic graph Transformer

Zhu Li1
Gao Jingkai1
Zhu Chunqiang2,3
Deng Fan1
1. School of Computer Science & Technology, Xi'an University of Science & Technology, Xi 'an 710054, China
2. School of Computer Science & Technology, Xi 'an Jiaotong University, Xi 'an 710049, China
3. State Grid Shaanxi Electric Power Company Training Center, Xi 'an 710032, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0231

Abstract

Accurate short-term load forecasting is vital for power system stability and scheduling, but the nonlinearity and nonstationary of power load data often reduce prediction accuracy. Decomposition can alleviate nonstationary, improving accuracy, yet existing methods struggle to capture relationships between components and increase prediction time. To address this issue, this paper introduced the component-aware dynamic graph Transformer(CDGT) model. It enhanced the snow ablation optimizer(SAO) with a joint opposite selection(JOS) operator and stochastic perturbation, resulting in a jointly searched and stochastic perturbed SAO(JSSAO) for optimizing variational mode decomposition(VMD). VMD decomposed load data into frequency-based components. GNN modeled relationships between components, while a Transformer with exponential moving average(EMA) attention captured internal dependencies. The model outputted all components in one step, with the final load forecast obtained by summing these components. Experiments on two public datasets show that CDGT outperforms advanced baselines, reducing MAE by 5.51%~31.08% on the Australian dataset and 15.02%~75.49% on the Moroccan dataset.

Foundation Support

国网陕西省电力有限公司科技项目(5226PX240003)
国网陕西电力有限公司数字化项目(B326PX230001,B326PX230000)
陕西省自然科学基础研究项目(2022JM317)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0231
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Special Topics in Trend Forecasting Based on Artificial Intelligence
Pages: 381-390
Serial Number: 1001-3695(2025)02-008-0381-10

Publish History

[2025-02-05] Printed Article

Cite This Article

朱莉, 高靖凯, 朱春强, 等. 基于分量感知动态图Transformer的短期电力负荷预测 [J]. 计算机应用研究, 2025, 42 (2): 381-390. (Zhu Li, Gao Jingkai, Zhu Chunqiang, et al. Short-term power load forecasting based on component-aware dynamic graph Transformer [J]. Application Research of Computers, 2025, 42 (2): 381-390. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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