Sensor Drift Calibration via Spatial Correlation Model in Smart Building
Proceedings of the 56th Annual Design Automation Conference 2019(2019)
摘要
Sensor drift is an intractable obstacle to practical temperature measurement in smart building. In this paper, we propose a sensor spatial correlation model. Given prior knowledge, Maximum-aposteriori (MAP) estimation is performed to calibrate drifts. MAP is formulated as a non-convex problem with three hyper-parameters. An alternating-based method is proposed to solve this non-convex formulation. Cross-validation and Expectation-maximum with Gibbs sampling are further to determine hyper-parameters. Experimental results show that on benchmarks from simulator EnergyPlus, compared with state-of-the-art method, the proposed framework can achieve a robust drift calibration and a better trade-off between accuracy and runtime.
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关键词
smart building,intractable obstacle,practical temperature measurement,MAP,nonconvex problem,alternating-based method,nonconvex formulation,cross-validation,robust drift calibration,sensor drift calibration,spatial correlation model,maximum-a-posteriori estimation,Gibbs sampling,expectation-maximum
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