SPROUTS HOME | FIRST TIME VISITORS | ABOUT | BOARD | GUIDE for AUTHORS | FAQ |CONTACT US
sprouts Login | Create Account AIS
Working Papers on Information Systems ISSN 1535-6078
Browse by: Year | Tag | Author | Organization | Proceedings
Advanced search

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

Charles C. Castello
Oak Ridge National Laboratory, USA
Joshua New
Oak Ridge National Laboratory, USA


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.

Full Text Document:
[img]
Preview
PDF 1597Kb
Reference:Castello, C. , New, J. (2012). "Autonomous Correction of Sensor Data Applied to Building Technologies Utilizing Statistical Processing Methods," Proceedings > Proceedings of Energy Informatics . Sprouts: Working Papers on Information Systems, 12(6). http://sprouts.aisnet.org/12-6
Keywords:Sensor data validation; statistical processing methods; least squares; maximum likelihood estimation; segmentation averaging; threshold based; building technologies.
Item Type:Article - Volume 12 Article 6 (2012)
Language:English
Email: Charles C. Castello (castellocc@ornl.gov)
Joshua New (newjr@ornl.gov)

Repository Staff Only: item control page

Show Tags

Tag this item: