Probabilistic graphical models in energy systems: A review

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.

Automated summary

Probabilistic graphical models are effective in addressing various issues in energy systems, with static models handling incomplete or uncertain information and dynamic models accurately predicting energy consumption, occupancy, and failures. A unified framework combining knowledge-driven and data-driven PGMs is suggested for better performance, with the need for universal PGM-based approaches adaptable to different energy systems and hybrid algorithms integrating advanced techniques for improved results.