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Predicting and Applying the Electricity-Related Carbon Emis..:

, In: 2024 International Conference on Power Electronics and Artificial Intelligence,

Zhang, Haoqin ; Lei, He ; Chi, Hetian... - p. 620-623 , 2024

Link: https://dl.acm.org/doi/10.1145/3674225.3674338

RT 
T1 <cTitle>2024 International Conference on Power Electronics and Artificial Intelligence</cTitle>: T1 <Title>Predicting and Applying the Electricity-Related Carbon Emission Coefficient based on Recurrent Neural Network: A Decision-Making Reference for Carbon Emission Policies</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3674338&Exemplar=1&LAN=DE
				
A1 Zhang, Haoqin
A1 Lei, He
A1 Chi, Hetian
A1 Zou, Chongzhe
A1 Liu, Tao
A1 Li, Siwu
A1 Zhou, Zhiqiang
A1 Zheng, Yunfei
A1 Dong, Mingqi
PB ACM
YR 2024
K1 Carbon Emission Policies
K1 Electricity-Related Carbon Emission Coefficient
K1 Green Investment
K1 Industrial Transformation
K1 Neural Network Modeling
K1 Applied computing
K1 Physical sciences and engineering
K1 Engineering
K1 Computer-aided design
K1 Computing methodologies
K1 Machine learning
K1 Machine learning algorithms
K1 Dynamic programming for Markov decision processes
K1 Approximate dynamic programming methods
SP 620
OP 623
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3674225.3674338
DO https://dl.acm.org/doi/10.1145/3674225.3674338
SF ELIB - SuUB Bremen

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