Short-Term Load Forecasting in Smart Grids Using Hybrid Deep Learning-Reference-Cited by-同舟云学术

Short-Term Load Forecasting in Smart Grids Using Hybrid Deep Learning

Published:2024 Issue: Volume:12 Page:23504-23513
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Asiri Mashael M.¹^ORCID,Aldehim Ghadah²^ORCID,Alotaibi Faiz Abdullah³^ORCID,Alnfiai Mrim M.⁴^ORCID,Assiri Mohammed⁵^ORCID,Mahmud Ahmed⁶

Affiliation:

1. Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi Arabia

2. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia

3. Department of Information Science, College of Arts, King Saud University, Riyadh, Saudi Arabia

4. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

5. Department of Computer Science, College of Sciences and Humanities Aflaj, Prince Sattam bin Abdulaziz University, Aflaj, Saudi Arabia

6. Research Center, Future University in Egypt, New Cairo, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10413463.pdf?arnumber=10413463

Reference33 articles.

1. An intelligent scheduling control method for smart grid based on deep learning

2. A novel cloud-edge collaboration based short-term load forecasting method for smart grid

3. A new approach to seasonal energy consumption forecasting using temporal convolutional networks

4. Deep Learning Based Load Forecasting for Futuristic Sustainable Smart Grid

5. Load Forecasting Techniques and Their Applications in Smart Grids

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