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1 Ergebnisse
1
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
2024 International Conference on Power Electronics and Artificial Intelligence
: T1
Predicting and Applying the Electricity-Related Carbon Emission Coefficient based on Recurrent Neural Network: A Decision-Making Reference for Carbon Emission Policies
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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