Temporal inception convolutional network based on multi‐head attention for ultra‐short‐term load forecasting-Reference-Cited by-同舟云学术

Temporal inception convolutional network based on multi‐head attention for ultra‐short‐term load forecasting

Published:2022-01-17 Issue:8 Volume:16 Page:1680-1696
ISSN:1751-8687
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation Trans & Dist

Author:

Tong Cheng¹^ORCID,Zhang Linghua¹²^ORCID,Li Hao¹,Ding Yin¹

Affiliation:

1. College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

2. Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Nanjing China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/gtd2.12394

Reference47 articles.

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3. The spatial electric load forecasting algorithm using the multiple regression analysis method;Nam B.W.;J. Korean Inst. IIIum. Electr. Install. Eng.,2008

4. Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models

5. Parsimonious Short-Term Load Forecasting for Optimal Operation Planning of Electrical Distribution Systems

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