Sensor Drift Calibration via Spatial Correlation Model in Smart Building

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-a-posteriori (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.