An electricity load forecasting model based on multilayer dilated LSTM network and attention mechanism

Frontiers in Energy Research

Energy Research

Tingting Zhang¹  Qi Dong¹  Ye Wang¹  Qiong Song²  Wenshuai Jiang²  Chong Wang³  Xueling Li⁴ 

[1] Power Supply Service Supervision and Support Center of East Inner Mongolia Electric Power Co., Ltd., Tongliao, China;School of Computer Science, Northeast Electric Power University, Jilin, China;School of Economics and Management, Northeast Electric Power University, Jilin, China;School of Management, Jilin University, Changchun, China;

关键词: neural network;    load forecasting;    seq2seq;    dilated LSTM;    attention mechanism;   

DOI  :  10.3389/fenrg.2023.1116465

 received in 2022-12-05, accepted in 2023-05-15,  发布年份 2023

来源: Frontiers

【 摘 要 】

From national development to daily life, electric energy is integral to people’s lives. Although the development of electricity should be expected, expansion without restriction will only result in energy waste. The forecasting of electricity load plays an important role in the adjustment of power enterprises’ strategies and the stability of power operation. Recently, the electricity-related data acquisition system has been perfected, and the available load information has gradually reached the minute level. This means that the related load series lengthens and the time and spatial information of load become increasingly complex. In this paper, a load forecasting model based on multilayer dilated long and short-term memory neural network is established. The model uses a multilayer dilated structure to extract load information from long series and to extract information from different dimensions. Moreover, the attention mechanism is used to make the model pay closer attention to the key information in the series as an intermediate variable. Such structures can greatly alleviate the loss in the extraction of long time series information and make use of more valid historical information for future load forecasting. The proposed model is validated using two real datasets. According to load forecasting curves, error curve, and related indices, the proposed method is more accurate and stable in electricity load forecasting than the comparison methods.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Jiang, Wang, Song, Zhang, Dong and Li.