Open AccessA Comparative Study of Deep Learning Frameworks Based on Short-term Power Load Forecasting Experiments
Author(s) -
Jingyu Zhang,
Yongjun Zhang,
Yanping Lu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012070
Subject(s) - term (time) , mainstream , artificial intelligence , deep learning , computer science , power (physics) , machine learning , political science , physics , quantum mechanics , law
This article introduces the results of comparative research on mainstream deep learning frameworks, including PyTorch, Keras, Tensorflow, and MXNet. Analyzed their similarities and differences, and compared their efficiency through short-term power load prediction experiments.