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℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon 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 thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto 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.

Perovskite single-pixel detector for dual-color metasurface imaging recognition in complex environment

Highly efficient multi-dimensional data storage and extraction are two primary ends for the design and fabrication of emerging optical materials.Although metasurfaces show great potential in information storage due to their modulation for different degrees of freedom of light,a compact and efficient detector for relevant multi-dimensional data retrieval is still a challenge,especially in complex environments.Here,we demonstrate a multi-dimensional image storage and retrieval process by using a dual-color metasurface and a double-layer integrated perovskite single-pixel detector(DIP-SPD).Benefitting from the photoelectric response characteristics of the FAPbBr2.4l0.6 and FAPbl3 films and their stacked structure,our filter-free DIP-SPD can accurately reconstruct different colorful images stored in a metasurface within a single-round measurement,even in complex environments with scattering media or strong background noise.Our work not only provides a compact,filter-free,and noise-robust detector for colorful image extraction in a metasurface,but also paves the way for color imaging application of perovskite-like bandgap tunable materials.