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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

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.

Original languageEnglish
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages121-124
Number of pages4
DOIs
StatePublished - 2013
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Conference

Conference2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Country/TerritoryUnited States
CityAustin, TX
Period12/3/1312/5/13

Keywords

  • Data analysis
  • Data processing
  • Filtering algorithms
  • Kalman filters
  • Sensor systems

Fingerprint

Dive into the research topics of 'Autonomous correction of sensor data applied to building technologies using filtering methods'. Together they form a unique fingerprint.

Cite this