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Multidimensional Feature-Based Graph Attention Networks and Dynamic Learning for Electricity Load Forecasting

Author

Listed:
  • Chaokai Huang

    (Guangdong Power Grid Co., Ltd., Shantou Supply Bureau, Shantou 515063, China)

  • Ning Du

    (Guangdong Power Grid Co., Ltd., Shantou Supply Bureau, Shantou 515063, China)

  • Jiahan He

    (Guangdong Power Grid Co., Ltd., Shantou Supply Bureau, Shantou 515063, China)

  • Na Li

    (Department of Computer Science, College of Engineering, Shantou University, Shantou 515063, China)

  • Yifan Feng

    (Department of Computer Science, College of Engineering, Shantou University, Shantou 515063, China)

  • Weihong Cai

    (Department of Computer Science, College of Engineering, Shantou University, Shantou 515063, China)

Abstract

Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time point, while the information on the time difference of the load data can reflect the dynamic evolution information of the load data, which is a very important factor for load forecasting. In addition, the research topics in recent years mainly focus on the learning of the complex relationships of load sequences in time latitude by graph neural networks. The relationships between different variables of load sequences are not explicitly captured. In this paper, we propose a model that combines a differential learning network and a multidimensional feature graph attention layer, it can model the time dependence and dynamic evolution of load sequences by learning the amount of load variation at different time points, while representing the correlation of different variable features of load sequences through the graph attention layer. Comparative experiments show that the prediction errors of the proposed model have decreased by 5–26% compared to other advanced methods in the UC Irvine Machine Learning Repository Electricity Load Chart public dataset.

Suggested Citation

  • Chaokai Huang & Ning Du & Jiahan He & Na Li & Yifan Feng & Weihong Cai, 2023. "Multidimensional Feature-Based Graph Attention Networks and Dynamic Learning for Electricity Load Forecasting," Energies, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6443-:d:1234142
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    References listed on IDEAS

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    1. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
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