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

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 t...

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Bibliographic Details
Published in2013 IEEE Global Conference on Signal and Information Processing pp. 121 - 124
Main Authors Castello, Charles C., New, Joshua R., Smith, Matt K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Buildings
data analysis
data processing
filtering algorithms
Humidity
Kalman filters
sensor systems
Temperature distribution
Temperature sensors
Testing
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Summary: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.
DOI:10.1109/GlobalSIP.2013.6736830