PhaCIA-TCNs: Short-Term Load Forecasting Using Temporal Convolutional Networks With Parallel Hybrid Activated Convolution and Input Attention-Reference-Cited by-同舟云学术

PhaCIA-TCNs: Short-Term Load Forecasting Using Temporal Convolutional Networks With Parallel Hybrid Activated Convolution and Input Attention

Published:2024-01 Issue:1 Volume:11 Page:427-438
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Xu Zhenghua¹^ORCID,Yu Zhoutao²^ORCID,Zhang Hexiang¹^ORCID,Chen Junyang³^ORCID,Gu Junhua⁴^ORCID,Lukasiewicz Thomas⁵^ORCID,Leung Victor C. M.³^ORCID

Affiliation:

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment and the School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China

2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China

3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

4. State Key Laboratory of Reliability and Intelligence of Electrical Equipment and the School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

5. Institute of Logic and Computation, TU Wien, Vienna, Austria

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province, China

Natural Science Foundation of Guangdong Province

Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy

Shenzhen Talents Special Project - Guangdong Provincial Innovation and Entrepreneurship Team Supporting

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/6488902/10382160/10198757.pdf?arnumber=10198757

Reference28 articles.

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4. Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

5. A Convolutional Encoder Model for Neural Machine Translation

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