Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management

Energies

Fan Yu¹  Lei Wang¹  Qunmin Yan¹  Shi Qiao¹  Qiaoyong Jiang² 

[1] College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an 710048, China;

关键词: smart grid;    short-term load forecasting;    feature engineering;    variational modal decomposition;    deep learning;    Informer;   

DOI  :  10.3390/en15124198

来源: DOAJ

【 摘 要 】

Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to optimize the model parameters. Cross-sectional and longitudinal experiments are conducted on a regional-level load dataset set in Spain. The experimental results prove that the proposed method is superior to other methods in STLF.

【 授权许可】

Unknown