Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (5): 135-146.doi: 10.3969/j.issn.1000-5641.2023.05.012

• Data Analytics • Previous Articles    

Research on Autoformer-based electricity load forecasting and analysis

Litao TANG1(), Zhiyong ZHANG1, Jun CHEN1, Linna XU2, Jiachen ZHONG2, Peisen YUAN2,*()   

  1. 1. Measurement Center of Guangxi Power Grid Co. Ltd., Nanning 530024, China
    2. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
  • Received:2023-06-30 Accepted:2023-07-22 Online:2023-09-25 Published:2023-09-15
  • Contact: Peisen YUAN;


Next-generation power grids is the main direction of future smart grid development, and the accurate prediction of power loads is an important basic task of smart grids. To improve the accuracy of load prediction in smart power systems, this work characterized the load dataset based on an Autoformer, a prediction model with an autocorrelation mechanism; adds a feature extraction layer to the original model; optimized the model parameters in terms of the number of coding layers, decoding layers, learning rate, and batch size; and achieved cycle-flexible load prediction. The experimental results show that the model performs better in prediction, with an MAE, MSE, and coefficient of determination of 0.2512, 0.1915, and 0.9832, respectively. Compared with other methods, this method has better load prediction results.

Key words: Autoformer, power load forecasting, autocorrelation mechanism, feature extraction, load characteristics

CLC Number: