CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Forecasting-Reference-Cited by-同舟云学术

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CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Forecasting

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Telecommunications Conference (ITC-Egypt)
language:
Short-container-title:

Author:

Farid Karim S.¹,Ali A.A.²,Salem Sameh A.³,Mohamed Amr E.¹

Affiliation:

1. Egyptian Electricity Holding Company,dept. Information Systems & Communications,Cairo,Egypt

2. Helwan University - Faculty of Engineering,dept. Electrical Power,Cairo,Egypt

3. Helwan University - Faculty of Engineering,dept. Computer & Systems Engineering,Cairo,Egypt

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10206047/10206045/10206214.pdf?arnumber=10206214

Reference29 articles.

1. Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

2. A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models

3. Decomposition-Residuals Neural Networks: Hybrid System Identification Applied to Electricity Demand Forecasting

4. Hourly Power Consumption Forecasting Using RobustSTL and TCN

5. Overcoming the Lack of Labeled Data: Training Intrusion Detection Models Using Transfer Learning

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