A Comparative Study of Photovoltaic Power Prediction Model with Different Inputs by Long Short-Term Memory Network-Reference-Cited by-同舟云学术

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A Comparative Study of Photovoltaic Power Prediction Model with Different Inputs by Long Short-Term Memory Network

Published:2023-09-21 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Power System Technology (PowerCon)
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Author:

Ma Jiao¹,Zhang Yunpeng¹,Zhou Hai²,Sun Shumin³,Wu Ji²,Hu Siyu²,Guan Yifei³,Yuan Shuai³

Affiliation:

1. School of Electrical Engineering, Shandong University,Jinan,Shandong Province,China,250061

2. China Electric Power Research Institute,Nanjing,Jiangsu Province,China,210003

3. State Grid Shandong Electric Power Co., Ltd.,Jinan,Shandong Province,China,250003

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330799/10330806/10331443.pdf?arnumber=10331443

Reference17 articles.

1. A novel method based on time series ensemble model for hourly photovoltaic power prediction

2. A PCA‐NPOGBDT strategy‐based protection scheme to differentiate between inverter and distribution line faults plus detection and identification of faulty section in microgrid

3. Solar energy: Potential and future prospects

4. A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting

5. Deep learning neural networks for short-term photovoltaic power forecasting

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