Short-Term Power Load Forecasting in FGSM-Bi-LSTM Networks Based on Empirical Wavelet Transform-Reference-Cited by-同舟云学术

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Short-Term Power Load Forecasting in FGSM-Bi-LSTM Networks Based on Empirical Wavelet Transform

Published:2023 Issue: Volume:11 Page:105057-105068
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Liu Qingchan¹,Cao Jianing²^ORCID,Zhang Jingcheng³^ORCID,Zhong Yao¹,Ba Tingjie¹,Zhang Yiming¹

Affiliation:

1. Measurement Center, Yunnan Power Grid Company Ltd., Kunming, China

2. Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming, China

3. Faculty of Science, Kunming University of Science and Technology, Kunming, China

Funder

Science and Technology Project of China Southern Power Grid Company Ltd.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10254217.pdf?arnumber=10254217

Reference33 articles.

1. Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network

2. A deep bi-directional long-short term memory neural network-based methodology to enhance short-term electricity load forecasting for residential applications

3. TransformGraph: A novel short-term electricity net load forecasting model

4. Electricity load forecasting and feature extraction in smart grid using neural networks

5. Adversarial attacks on deep neural networks for time series classification;fawaz;Proc Int Joint Conf Neural Netw (IJCNN),2019

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