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Probabilistic graphical models in energy systems: A review

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Abstract

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 systems. Compared with the black-box models, PGMs show advantages in model interpretability, scalability and reliability. They have great potential to realize the true artificial intelligence in energy systems of the next generation. This paper intends to provide a comprehensive review of the PGM-based approaches published in the last decades. It reveals the advantages, limitations and potential future research directions of the PGM-based approaches for energy systems. Two 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 information. SPGM-based approaches are proposed to deal with various management tasks in energy systems. They show outstanding performance in fault detection and diagnosis of energy systems. DPGMs can represent a dynamic and stochastic process by describing how its state changes with time. DPGM-based approaches have high accuracy in predicting the energy consumption, occupancy and failures of energy systems. In the future, a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better performances. Universal PGM-based approaches are needed that can be adapted to various energy systems. Hybrid algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.

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Abbreviations

AI:

artificial intelligence

AHU:

air handling unit

DPGM:

dynamic probabilistic graphical model

HVAC:

heating, ventilation and air conditioning

PGM:

probabilistic graphical model

PV:

photovoltaic

SPGM:

static probabilistic graphical model

VAV:

variable air volume

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2018YFE0116300]; and the National Natural Science Foundation of China (No. 51978601).

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Authors and Affiliations

  1. Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China

    Tingting Li, Yang Zhao, Chaobo Zhang & Xuejun Zhang

  2. Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore, Singapore

    Ke Yan

  3. Zhejiang Energy Gas Group Co., Ltd, Hangzhou, China

    Kai Zhou

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  1. Tingting Li
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Correspondence to Yang Zhao.

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Li, T., Zhao, Y., Yan, K. et al. Probabilistic graphical models in energy systems: A review. Build. Simul. 15, 699–728 (2022). https://doi.org/10.1007/s12273-021-0849-9

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