A Method for Predicting Carbon Emission Intensity in Integrated energy system Based on Federated Learning-Reference-Cited by-同舟云学术

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A Method for Predicting Carbon Emission Intensity in Integrated energy system Based on Federated Learning

Published:2023-12-08 Issue: Volume:42 Page:1589-1592
ISSN:
Container-title:2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST)
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Author:

Shen Liang¹,Zhou Chunlei¹,Liu Wensi¹,Li Qiang²,Hao Yanya³,He Dong⁴

Affiliation:

1. Big Data Center of State Grid Co., LTD,Beijing,China

2. State Grid Information and Communication Industry Group CO., LTD,Beijing,China

3. Beijing China-Power Information Technology CO., LTD,Beijing,China

4. Information and Communication Branch of State Grid Zhejiang Electric Power Co., LTD,Hangzhou,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10502394/10502376/10502665.pdf?arnumber=10502665

Reference6 articles.

1. Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting

2. Short-term load forecasting method based on deep belief network [J];Xiangyu;Power System Automation,2018

3. Short-term Load Forecasting based on Deep Belief Network[J];Xiangyu;Automation of Electric Power Systems,2018

4. An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders

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