NDLI: Machine Learning Techniques Applied to Sensor Data Correction in Building Technologies

Content Provider	IEEE Xplore Digital Library
Author	Smith, M.K. Castello, C.C. New, J.R.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscaloosa, AL, USA (Smith, M.K.) \|\| Energy & Transp. Sci. Div., Oak Ridge Nat. Lab., Oak Ridge, TN, USA (Castello, C.C.; New, J.R.)
Abstract	Since commercial and residential buildings account for nearly half of the United States' energy consumption, making them more energy-efficient is a vital part of the nation's overall energy strategy. Sensors play an important role in this research by collecting data needed to analyze performance of components, systems, and whole-buildings. Given this reliance on sensors, ensuring that sensor data are valid is a crucial problem. The solution we are researching is machine learning techniques, namely: artificial neural networks and Bayesian Networks. Types of data investigated in this study are: (1) temperature, (2) humidity, (3) refrigerator energy consumption, (4) heat pump liquid pressure, and (5) water flow. These data are taken from Oak Ridge National Laboratory's (ORNL) ZEBRAlliance research project which is composed of four single-family homes in Oak Ridge, TN. Results show that for the temperature, humidity, pressure, and flow sensors, data can mostly be predicted with root-mean-square error of less than 10% of the respective sensor's mean value. Results for the energy sensor were not as good, root-mean-square errors were centered about 100% of the mean value and were often well above 200%. Bayesian networks had smaller errors, but took substantially longer to train.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	305
Ending Page	308
File Size	552621
Page Count	4
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.62
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature sensors artificial neural networks Liquids Refrigerators Buildings sensor data validation building technologies Humidity Robot sensing systems Bayes methods Bayesian networks machine learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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