Electrical Engineering  2024, Vol. 25 Issue (10): 8-14    DOI:
Research & Development Current Issue| Next Issue| Archive| Adv Search |
Short term power load forecasting based on temporal convolutional network-long short term memory and improved attention mechanism
LIU Wei, WANG Hongzhi
College of Electrical Information Engineering, Northeast Petroleum University, Daqing, Heilongjiang 163000

Download: PDF (1851 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  To fully explore the effective temporal information contained in power load data and improve the accuracy of short term power load prediction, this article proposes a power load forecasting model based on an improved attention mechanism for the temporal convolutional network (TCN)-long short term memory (LSTM) network. Firstly, the temporal data is input into the TCN model to extract temporal features. Then, the extracted temporal features are combined with non temporal to be input into the LSTM model for training. Finally, Bayesian optimization method is used for hyperparameter optimization to get the best parameters in TCN-LSTM. An attention mechanism improved by multi-layer perceptron (MLP) is introduced to reduce the loss of historical information and strengthen the influence of important information, completing short term load forecasting. By comparing the predictive performance of various deep learning models, it is verified that the model proposed in this article has higher accuracy in short term power load forecasting.
Key wordsshort term power load forecasting      improved attention mechanism      Bayesian optimization      multi-layer perceptron (MLP)      time convolutional network (TCN)      long short term memory (LSTM) network     
Received: 02 April 2024     
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
LIU Wei
WANG Hongzhi
Cite this article:   
LIU Wei,WANG Hongzhi. Short term power load forecasting based on temporal convolutional network-long short term memory and improved attention mechanism[J]. Electrical Engineering, 2024, 25(10): 8-14.
URL:  
https://dqjs.cesmedia.cn/EN/Y2024/V25/I10/8
Copyright © Electrical Engineering
Supported by: Beijing Magtech