Autonomous correction of sensor data applied to building technologies using filtering methods

Author

Castello, C.C. ; New, Joshua R. ; Smith, Matt K.

Author_Institution

Energy Transp. & Sci. Div., Oak Ridge Nat. Lab., Oak Ridge, TN, USA

fYear

2013

fDate

3-5 Dec. 2013

Firstpage

121

Lastpage

124

Abstract

Sensor data validity is extremely important in a number of applications, particularly building technologies. An example of this is Oak Ridge National Laboratory´s ZEBRAlliance research project, which consists of four single-family homes located in Oak Ridge, TN. The homes are outfitted with a total of 1,218 sensors to determine the performance of a variety of different technologies integrated within each home. Issues arise with such a large amount of sensors, such as missing or corrupt data. This paper aims to eliminate these problems using: (1) Kalman filtering and (2) linear predictive coding (LPC) techniques. Simulations show the Kalman filtering method performed best in predicting temperature, humidity, pressure, and airflow data, while the LPC method performed best with energy consumption data.

Keywords

Kalman filters; building management systems; electrical engineering computing; energy consumption; linear predictive coding; sensor fusion; Kalman filtering method; LPC; Oak Ridge National Laboratory ZEBRAlliance research project; airflow data; autonomous sensor data correction; building technology; energy consumption data; humidity prediction; linear predictive coding techniques; pressure prediction; sensor data validation; temperature prediction; Buildings; Humidity; Kalman filters; Temperature distribution; Temperature sensors; Testing; Kalman filters; data analysis; data processing; filtering algorithms; sensor systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE

Conference_Location

Austin, TX

Type

conf

DOI

10.1109/GlobalSIP.2013.6736830

Filename

6736830