A hybrid method for day‐ahead photovoltaic power forecasting based on generative adversarial network combined with convolutional autoencoder-Reference-Cited by-同舟云学术

A hybrid method for day‐ahead photovoltaic power forecasting based on generative adversarial network combined with convolutional autoencoder

Published:2022-10-21 Issue:3 Volume:17 Page:644-658
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Gen

Author:

Pan Xueping¹^ORCID,Zhou Jieyang¹,Sun Xiaorong¹,Cao Yang¹,Cheng Xiaomei²,Farahmand Hossein³^ORCID

Affiliation:

1. College of Energy and Electrical Engineering HoHai University Nanjing China

2. Department of Research and Development Yara International Oslo Norway

3. Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway

Funder

National Key Research and Development Program of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/rpg2.12619

Reference41 articles.

1. Forecasting of photovoltaic power generation and model optimization: A review

2. National Energy Administration.National electric power industry statistics from. January to March (2021)http://www.nea.gov.cn/2021‐05/20/c_139958706.htm

3. Impact of Distributed Rooftop Photovoltaic Systems on Short-Circuit Faults in the Supplying Low Voltage Networks

4. Review of photovoltaic power forecasting

5. Physical and hybrid methods comparison for the day ahead PV output power forecast

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