Short-term PV Output Power Forecasting Based on CEEMDAN-AE-GRU-Reference-Cited by-同舟云学术

Short-term PV Output Power Forecasting Based on CEEMDAN-AE-GRU

Published:2022-01-06 Issue:2 Volume:17 Page:1183-1194
ISSN:1975-0102
Container-title:Journal of Electrical Engineering & Technology
language:en
Short-container-title:J. Electr. Eng. Technol.

Author:

Zhang Na,Ren Qiang^ORCID,Liu Guangchen,Guo Liping,Li Jingyu

Funder

National Natural Science Foundation of China

Natural Science Foundation of Inner Mongolia

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42835-021-00975-3.pdf

Reference22 articles.

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2. Munawar U, Wang Z (2020) A framework of using machine learning approaches for short-term solar power forecasting. J Electric Eng Technol 15(2):561–569

3. Wang S, Liang D, Ge L (2016) Key technologies of situation awareness and orientation for smart distribution systems. Autom Electric Power Syst 40(12):2–8

4. Gong Y, Lu Z, Qiao Y, Wang Q (2016) An overview of photovoltaic energy system output forecasting technology. Autom Electric Power Syst 40(04):140–151

5. Huang C-J, Kuo P-H (2019) Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE Access 7:74822–74834

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