A review of the application of deep learning in power load forecasting
[1]Zhu Juncheng,Young Joy,Guo Yuanjun,et al.A review of the application of deep learning in power load forecasting[J].Journal of Zhengzhou University (Engineering Science),2019,40(05):12-21.[doi:10.13705/j.issn.1671-6833.2019.05.005]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40卷
Number of periods:
2019 05
Page number:
12-21
Column:
Public date:
2019-10-23
- Title:
- A review of the application of deep learning in power load forecasting
- Keywords:
- Deep learning ; power system ; load forecasting ; artificial neural network ; LSTM
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2019.05.005
- Abstract:
- In the rapid development of integrated energy systems and energy network, power load forecasting played an important role in the economic and safe operation of energy and power systems. The traditional load forecasting modelling methods have been widely used in power systems. However, the simple computational model structure limited by traditional methods could not guarantee the dynamic load prediction accuracy under high randomness and big data background. In recent years, in the context of the continuous upgrading of com puting tools and the increasing large-scale of training data volume, the application of deep learning methods in the field of power system load forecasting atrracted extensive attentions. This paper analyzed the applications of various deep learning methods in the field of load forecasting, and revieed the Recurrent Neural Network (RNN) , Long- and Short-Term Memory Network ( LSTM) , Deep Belief Network ( DBN) , and Convolutional Neural Network ( CNN). Compared with the traditional load forecasting method, the deep learning method showed higher prediction accuracy and better robustness to various external influences.
- References:
- -
- Similar References:
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Last Update:
2019-10-26