Strategies for sensor virtual in-situ calibration in building energy system: Sensor evaluation and data-driven based methods

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

doi:10.1016/j.enbuild.2023.113274

Cited by 5 publications

(1 citation statement)

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“…This method improved the accuracy by 2.8–20% compared with the energy conservation Bayesian inference and principal component analysis methods. Li et al [ 35 ] applied a Bayesian-inference-based method to a practical building energy system and demonstrated calibration accuracy and efficiency improvement using this strategy. Tian et al [ 36 ] proposed a sensor drifting bias calibration method based on Bayesian inference and an autoencoder.…”

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%