文献类型：期刊文献

英文题名：ELFNet: An Effective Electricity Load Forecasting Model Based on a Deep Convolutional Neural Network with a Double-Attention Mechanism

作者：Zhao, Pei[1];Ling, Guang[2];Song, Xiangxiang[2]

通讯作者：Ling, G[1];Song, XX[1]

机构：[1]Beijing Union Univ, Teachers Coll, Beijing 100023, Peoples R China;[2]Wuhan Univ Technol, Sch Sci, Wuhan 430070, Peoples R China

第一机构：北京联合大学师范学院

通讯机构：[1]corresponding author), Wuhan Univ Technol, Sch Sci, Wuhan 430070, Peoples R China.

年份：2024

卷号：14

期号：14

外文期刊名：APPLIED SCIENCES-BASEL

收录：;EI(收录号：20243116778448);Scopus(收录号：2-s2.0-85199636232);WOS:【SCI-EXPANDED(收录号:WOS:001276599400001)】；

基金：This work was partially supported by the National Natural Science Foundation of China under Grants 61503282 and 62073301, the Fundamental Research Funds for the Central Universities (WUT: 2021III062JC).

语种：英文

外文关键词：electricity load forecasting; deep convolutional neural network; channel attention; spatial attention; Gramian angular field

摘要：Forecasting energy demand is critical to ensure the steady operation of the power system. However, present approaches to estimating power load are still unsatisfactory in terms of accuracy, precision, and efficiency. In this paper, we propose a novel method, named ELFNet, for estimating short-term electricity consumption, based on the deep convolutional neural network model with a double-attention mechanism. The Gramian Angular Field method is utilized to convert electrical load time series into 2D image data for input into the proposed model. The prediction accuracy is greatly improved through the use of a convolutional neural network to extract the intrinsic characteristics from the input data, along with channel attention and spatial attention modules, to enhance the crucial features and suppress the irrelevant ones. The present ELFNet method is compared to several classic deep learning networks across different prediction horizons using publicly available data on real power demands from the Belgian grid firm Elia. The results show that the suggested approach is competitive and effective for short-term power load forecasting.