Autonomous Correction of Sensor Data Applied to Building Technologies Utilizing Statistical Processing Methods

Abstract

Autonomous detection and correction of potentially missing or corrupt sensor data is a essential concern in building technologies since data availability and correctness is necessary to develop accurate software models for instrumented experiments. Therefore, this paper aims to address this problem by using statistical processing methods including: (1) least squares; (2) maximum likelihood estimation; (3) segmentation averaging; and (4) threshold based techniques. Application of these validation schemes are applied to a subset of data collected from Oak Ridge National Laboratory’s (ORNL) ZEBRAlliance research project, which is comprised of four single-family homes in Oak Ridge, TN outfitted with a total of 1,218 sensors. The focus of this paper is on three different types of sensor data: (1) temperature; (2) humidity; and (3) energy consumption. Simulations illustrate the threshold based statistical processing method performed best in predicting temperature, humidity, and energy data.