TY - GEN
T1 - Autonomous correction of sensor data applied to building technologies using filtering methods
AU - Castello, Charles C.
AU - New, Joshua R.
AU - Smith, Matt K.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Data analysis
KW - Data processing
KW - Filtering algorithms
KW - Kalman filters
KW - Sensor systems
UR - http://www.scopus.com/inward/record.url?scp=84897728446&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6736830
DO - 10.1109/GlobalSIP.2013.6736830
M3 - Conference contribution
AN - SCOPUS:84897728446
SN - 9781479902484
T3 - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
SP - 121
EP - 124
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
T2 - 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Y2 - 3 December 2013 through 5 December 2013
ER -