Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

Wu, Rouhui; Ren, Yizhu; Tan, Mengying; Nie, Lei

doi:10.1007/s12273-023-1086-1

Build. Simul.

2024

DOI: 10.1007/s12273-023-1086-1

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Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

Rouhui Wu,

Yizhu Ren,

Mengying Tan

et al.

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Cited by 6 publications

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Interpretable data-driven fault diagnosis method for data centers with composite air conditioning system

Zhang,

Tao,

Qiu

et al. 2024

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Interpretable data-driven fault diagnosis method for data centers with composite air conditioning system

Zhang,

Tao,

Qiu

et al. 2024

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Generic load regulation strategy for enhancing energy efficiency of chiller plants

Wan,

Gong,

Wang

et al. 2024

Build. Simul.

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In many chiller plants, high coefficient of performance (COP) is only achieved at a few favorable part load ratios (PLRs), while the COP is low at many other non-favorable PLRs. To address this issue, this study proposes a generic load regulation strategy that aims to maintain chiller plants operating at high COP, particularly under non-favorable PLRs. This is achieved by incorporating thermal energy storage (TES) units and timely optimizing the charging and discharging power of the integrated TES units. The optimal charging and discharging power is determined by solving a dynamic optimization problem, taking into account the performance constraints of the TES units and the chiller plants. To provide an overview of the energy-saving potential of the proposed strategy, a comprehensive analysis was conducted, considering factors such as building load profiles, COP/PLR curves of chillers, and attributes of the TES units. The analysis revealed that the proposed load regulation strategy has the potential to achieve energy savings ranging from 5.7% to 10.8% for chiller plants with poor COPs under unfavorable PLRs, particularly in buildings with significant load variations.

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Semi-supervised CWGAN-GP modeling for AHU AFDD with high-quality synthetic data filtering mechanism

Wang,

Bi,

Hua

et al. 2025

Building and Environment

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Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

Cited by 6 publications

References 35 publications

Interpretable data-driven fault diagnosis method for data centers with composite air conditioning system

Interpretable data-driven fault diagnosis method for data centers with composite air conditioning system

Generic load regulation strategy for enhancing energy efficiency of chiller plants

Semi-supervised CWGAN-GP modeling for AHU AFDD with high-quality synthetic data filtering mechanism

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