Diagnostic Bayesian network in building energy systems

Current insights, practical challenges, and future trends

Review (2025)
Author(s)

C.J. Lu (TU Delft - Environmental & Climate Design)

Z. Wang (TU Delft - Environmental & Climate Design)

Martin Mosteiro-Romero (TU Delft - Environmental & Climate Design)

L. C.M. Itard (TU Delft - Environmental & Climate Design)

Research Group
Environmental & Climate Design
DOI related publication
https://doi.org/10.1016/j.enbuild.2025.115845
More Info
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Publication Year
2025
Language
English
Research Group
Environmental & Climate Design
Volume number
341
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Abstract

Many buildings suffer from operational inefficiencies, leading to uncomfortable indoor environments, poor air quality, and significant energy waste. Developing automatic fault detection and diagnosis (FDD) tools in building energy systems is essential to mitigate these issues, reducing both energy waste and maintenance costs. Diagnostic Bayesian networks (DBNs), as probabilistic graphical models, offer a promising solution due to their interpretability, robustness to uncertainty, scalability, and flexibility. In this paper, the practical applications of DBNs for FDD in building energy systems are comprehensively reviewed. The generic modeling procedure is systematically examined and summarized, covering problem formulation, structure modeling, parameter modeling, and fault isolation and evaluation. Then, the paper provides insights into DBN modeling objectives, modeling types, diagnostic samples, and modeling software based on the 43 key relevant papers. Furthermore, the paper discusses practical challenges such as sensor configuration, baseline estimation, threshold determination, and expert knowledge integration. Finally, the recommendations are provided to guide further research, aiming to enhance DBN implementation for building energy systems in real-world scenarios, thereby supporting the transformation of the building service industry into a smart sector and ultimately improving building energy performance.