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A Stochastic Online Forecast-and-Optimize Framework for Rea..:

, In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management,

Jiang, Wei ; Yi, Zhongkai ; Wang, Li... - p. 4646-4652 , 2023

Link: https://dl.acm.org/doi/10.1145/3583780.3614653

RT 
T1 <cTitle>Proceedings of the 32nd ACM International Conference on Information and Knowledge Management</cTitle>: T1 <Title>A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3614653&Exemplar=1&LAN=DE
				
A1 Jiang, Wei
A1 Yi, Zhongkai
A1 Wang, Li
A1 Zhang, Hanwei
A1 Zhang, Jihai
A1 Lin, Fangquan
A1 Yang, Cheng
PB ACM
YR 2023
K1 AI for social good
K1 agent-based systems
K1 deep learning
K1 planning and scheduling
K1 stochastic optimization
K1 uncertainty in AI
K1 Computing methodologies
K1 Artificial intelligence
K1 Planning and scheduling
K1 Planning under uncertainty
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Applied computing
K1 Operations research
K1 Decision analysis
K1 Multi-criterion optimization and decision-making
K1 Forecasting
SP 4646
OP 4652
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3583780.3614653
DO https://dl.acm.org/doi/10.1145/3583780.3614653
SF ELIB - SuUB Bremen

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