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Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems
For building heating, ventilation and air-conditioning systems (HVACs), sensor faults significantly affect the operation and control. Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand. Owing to its high calibration accuracy and in-situ effectiveness, a virtual sensor (VS)-assisted Bayesian inference (VS-BI) sensor calibration strategy has been applied for HVACs. However, the application feasibility of this strategy for wider ranges of different sensor types (within-control-loop and out-of-control-loop) with various sensor bias fault amplitudes, and influencing factors that affect the practical in-situ calibration performance are still remained to be explored. Hence, to further validate its in-situ calibration performance and analyze the influencing factors, this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit (AHU) terminal. Three target sensors including air supply (SAT), chilled water supply (CHS) and cooling water return (CWR) temperatures are investigated using introduced sensor bias faults with eight different amplitudes of [−2 °C, +2 °C] with a 0.5 °C interval. Calibration performance is evaluated by considering three influencing factors: (1) performance of different data-driven VSs, (2) the influence of prior standard deviations σ on in-situ sensor calibration and (3) the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes. After comparison, a long short term memory (LSTM) is adopted for VS construction with determination coefficient R-squared of 0.984. Results indicate that σ has almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR. The potential of using a prior standard deviation σ to improve the calibration accuracy is limited, only 8.61% on average. For system within-control-loop sensors like SAT and CHS, VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.
Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems
For building heating, ventilation and air-conditioning systems (HVACs), sensor faults significantly affect the operation and control. Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand. Owing to its high calibration accuracy and in-situ effectiveness, a virtual sensor (VS)-assisted Bayesian inference (VS-BI) sensor calibration strategy has been applied for HVACs. However, the application feasibility of this strategy for wider ranges of different sensor types (within-control-loop and out-of-control-loop) with various sensor bias fault amplitudes, and influencing factors that affect the practical in-situ calibration performance are still remained to be explored. Hence, to further validate its in-situ calibration performance and analyze the influencing factors, this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit (AHU) terminal. Three target sensors including air supply (SAT), chilled water supply (CHS) and cooling water return (CWR) temperatures are investigated using introduced sensor bias faults with eight different amplitudes of [−2 °C, +2 °C] with a 0.5 °C interval. Calibration performance is evaluated by considering three influencing factors: (1) performance of different data-driven VSs, (2) the influence of prior standard deviations σ on in-situ sensor calibration and (3) the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes. After comparison, a long short term memory (LSTM) is adopted for VS construction with determination coefficient R-squared of 0.984. Results indicate that σ has almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR. The potential of using a prior standard deviation σ to improve the calibration accuracy is limited, only 8.61% on average. For system within-control-loop sensors like SAT and CHS, VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.
Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems
Build. Simul.
Li, Guannan (author) / Xiong, Jiahao (author) / Sun, Shaobo (author) / Chen, Jian (author)
Building Simulation ; 16 ; 185-203
2023-02-01
19 pages
Article (Journal)
Electronic Resource
English
heating, ventilation and air-conditioning (HVAC) , in-situ sensor calibration , Bayesian inference (BI) , virtual sensor (VS) , influencing factor , energy conservation (EC) Engineering , Building Construction and Design , Engineering Thermodynamics, Heat and Mass Transfer , Atmospheric Protection/Air Quality Control/Air Pollution , Monitoring/Environmental Analysis
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