An Edge-AI Based Forecasting Approach for Improving Smart Microgrid Efficiency-Reference-Cited by-同舟云学术

An Edge-AI Based Forecasting Approach for Improving Smart Microgrid Efficiency

Published:2022-11 Issue:11 Volume:18 Page:7946-7954
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Lv Lingling¹^ORCID,Wu Zongyu¹,Zhang Lei²^ORCID,Gupta Brij B.³^ORCID,Tian Zhihong⁴^ORCID

Affiliation:

1. Institute of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou, China

2. Henan Key Laboratory of Big Data Analysis and Processing, Institute of Data and Knowledge Engineering, School of Computer and Information Engineering, Henan University, Kaifeng, China

3. Department of Computer Science and Information Engineering, Asia University, Taichung, China

4. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Scientific and Technological Innovation Team of Colleges and Universities in Henan Province

Excellent Youth Fund of Henan Natural Science Foundation

Henan High Level Talent Support Plan

Guangdong Province Universities

Colleges Pearl River Scholar Funded Scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9895125/09744454.pdf?arnumber=9744454

Reference25 articles.

1. Adaptive Feature Selection and Construction for Day-Ahead Load Forecasting Use Deep Learning Method

2. Predicting Weather-Related Failure Risk in Distribution Systems Using Bayesian Neural Network

3. A novel composite electricity demand forecasting framework by data processing and optimized support vector machine

4. Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting

5. Wind power forecasting – A data-driven method along with gated recurrent neural network

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