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

Fan Yu, Lei Wang, Qiaoyong Jiang, Qunmin Yan and Shi Qiao
Additional contact information
Fan Yu: College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China
Lei Wang: College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China
Qiaoyong Jiang: Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an 710048, China
Qunmin Yan: College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China
Shi Qiao: College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China

Energies, 2022, vol. 15, issue 12, 1-19

Abstract: 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.

Keywords: smart grid; short-term load forecasting; feature engineering; variational modal decomposition; deep learning; Informer; AdaBelief (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/12/4198/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/12/4198/ (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:jeners:v:15:y:2022:i:12:p:4198-:d:833496

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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