Improvement for energy efficiency and control characteristics in variable air volume system using in-situ sensor calibration method based on autoencoder

Li, Jiteng; Wang, Peng; Xing, Han; Zhao, Tianyi; Yoon, Sung‐Min

doi:10.1016/j.jobe.2022.105559

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…Zhao et al [ 30 ] combined a virtual sensor in situ calibration method with Gaussian mixture model clustering to realize the online diagnosis and real-time repair of sensors in air-handling units. Li et al [ 31 ] used an autoencoder to establish a sensor model for a virtual sensor in situ calibration method. Then, they discussed the calibration performance for a single fault and concurrent faults, with sensor accuracies of more than 90.75% and 88.5%, respectively, and the energy efficiency was increased by 21% and 17% for the water pump and the supply fan, respectively.…”

Section: Introductionmentioning

confidence: 99%

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Li,

Wang,

Liu

et al. 2024

Sensors

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A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

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Section: Introductionmentioning

confidence: 99%