Leveraging Spatial Correlation for Sensor Drift Calibration in Smart Building

Abstract

Sensor drift is an intractable obstacle to practical temperature measurement in smart building. In this article, 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 nonconvex problem with three hyper-parameters. An alternating-based method is proposed to solve this nonconvex formulation. Cross-validation, Gibbs expectation-maximization (EM) and variational Bayesian EM (VB-EM) are further exploited to determine hyper-parameters. Experimental results on widely used benchmarks from the simulator EnergyPlus demonstrate that compared with state-of-the-art methods, the proposed framework can achieve a robust drift calibration and a better tradeoff between accuracy and runtime. On average, compared with state-of-the-art, the proposed framework can achieve about 3 × accuracy improvement. In order to attain the same drift calibration accuracy with VB-EM, Gibbs EM needs 10 000 samples, which will incur a 30 × runtime overhead.