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Probabilistic graphical models in energy systems:A review

Building Simulation 2022年第5期15卷 699-728页

作者： Tingting Li Yang Zhao Ke Yan Kai Zhou Chaobo Zhang Xuejun Zhang Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Department of Building School of Design and EnvironmentNational University of Singapore4 Architecture DriveSingapore Zhejiang Energy Gas Group Co. LtdHangzhouChina

Probabilistic graphical models(PGMs)can effectively deal with the problems of energy consumption and occupancy prediction,fault detection and diagnosis,reliability analysis,and optimization in energy *** with the black-box models,PGMs show advantages in model interpretability,scalability and *** have great potential to realize the true artificial intelligence in energy systems of the next *** paper intends to provide a comprehensive review of the PGM-based approaches published in the last *** reveals the advantages,limitations and potential future research directions of the PGM-based approaches for energy *** types of PGMs are summarized in this review,including static models(SPGMs)and dynamic models(DPGMs).SPGMs can conduct probabilistic inference based on incomplete,uncertain or even conflicting ***-based approaches are proposed to deal with various management tasks in energy *** show outstanding performance in fault detection and diagnosis of energy *** can represent a dynamic and stochastic process by describing how its state changes with ***-based approaches have high accuracy in predicting the energy consumption,occupancy and failures of energy *** the future,a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better *** PGM-based approaches are needed that can be adapted to various energy *** algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.

关键词： probabilistic graphical model energy system bayesian network-dynamic bayesian network Markov chain hidden Markov model

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