• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

author:

Li, J. (Li, J..) [1] | Zhao, Z. (Zhao, Z..) [2] | Jin, T. (Jin, T..) [3]

Indexed by:

Scopus

Abstract:

With the rapid development of smart grids, accurate load prediction is essential for stable operation and optimal scheduling. This paper addresses errors, missing values, and anomalies in electrical load data using a conditional generative adversarial network (CGAN) with dual self-attention (SELF) for data reconstruction. The model simplifies time-series complexity and historical load patterns, eliminating the need for intricate spatiotemporal modeling. Based on the reconstructed data, a short-term load forecasting method is proposed using a bidirectional temporal convolutional network (BiTCN), bidirectional gated recurrent unit (BiGRU), and self-attention. This model processes forward and backward time-series information in parallel, extracting multi-scale features for more accurate predictions. In order to accurately describe the response behavior of users under different electricity price differentials, a logistic demand response (DR) model considering time lag factors is introduced. The model defines optimistic and pessimistic response curves, effectively reflecting the actual range of user responses to price incentives, thus enhancing the practicality of load forecasting in decision support. Experimental results demonstrate that the proposed method not only enhances the accuracy and stability of load forecasting but also provides robust technical support for the stable operation of smart grids. © 2025

Keyword:

Attention mechanism Data reconstruction Deep learning Demand response Short-term load forecasting

Community:

  • [ 1 ] [Li J.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
  • [ 2 ] [Zhao Z.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
  • [ 3 ] [Jin T.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China

Reprint 's Address:

Email:

Show more details

Related Keywords:

Source :

Expert Systems with Applications

ISSN: 0957-4174

Year: 2025

Volume: 292

7 . 5 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

Affiliated Colleges:

Online/Total:352/10753827
Address:FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code:350116) Contact Us:0591-22865326
Copyright:FZU Library Technical Support:Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1