Multi-layer BiLSTM Photovoltaic Prediction Study Based on Improved PSO-Reference-Cited by-同舟云学术

Multi-layer BiLSTM Photovoltaic Prediction Study Based on Improved PSO

Published:2023-10-27 Issue: Volume: Page:
ISSN:
Container-title:2023 5th International Conference on Circuits and Systems (ICCS)
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Author:

Liu Yaxi¹,Liu Di²,Li Wenjing³,Guo Shaoyong¹,Qi Feng¹

Affiliation:

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications,Beijing,China

2. Information Industry Research Institute, State Grid Information and Telecommunication Group Company Ltd,Beijing,China

3. Intelligent terminal R&D center, State Grid Information and Telecommunication Group Company Ltd,Beijing,China

Funder

National Key Research and Development Program of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10367210/10367213/10367381.pdf?arnumber=10367381

Reference23 articles.

1. The application and prospect of artificial intelligence technology in new energy power prediction;Qiongfeng;Chinese Journal of Electrical Engineering,2023

2. A review of combined approaches for prediction of short-term wind speed and power

3. A sequential ensemble model for photovoltaic power forecasting

4. Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge

5. Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting

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