Loading…
Labeling Modes of Operation and Extracting Features for Fault Detection with Cloud-Based Thermostat Data
This paper presents a method for transforming raw cloud-based thermostat data for cycling systems into a set of operating modes that is useful for large scale data analysis. Thermostat data typically includes the setpoint temperatures, the actual indoor temperature and the operating mode. These raw...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 79 |
| container_issue | 2 |
| container_start_page | 71 |
| container_title | |
| container_volume | 126 |
| creator | Rogers, Austin Guo, Fangzhou Ma Rasmussen, Bryan |
| description | This paper presents a method for transforming raw cloud-based thermostat data for cycling systems into a set of operating modes that is useful for large scale data analysis. Thermostat data typically includes the setpoint temperatures, the actual indoor temperature and the operating mode. These raw thermostat operating modes include "cooling on' "heating on', and "system off. The transformed operating modes include regulating modes, tracking modes, and free response modes. These new modes, which can be generated during data preprocessing, are used to more clearly show key system performance metrics and identify change points in the time series thermostat data. This paper includes the filtering logic used to label the operating modes and examples of insightful behavior that has been captured using this preprocessing method. |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2489352914</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A650330211</galeid><sourcerecordid>A650330211</sourcerecordid><originalsourceid>FETCH-LOGICAL-g222t-395c4203a072ec2abf57d6818be79c66ba4c363fd77660d7b67c14957dfed1bf3</originalsourceid><addsrcrecordid>eNotz01PAjEQBuA9aCKi_6GJ5zX9LntEBDXBcMEzmW2nsGTZYtuN_nyrmDlM5s2TmcxVNaGUsporqm6q25SOZZJK6kl1WEOLfTfsyXtwmEjwZHPGCLkLA4HBkeV3jmDzr1gh5DEW5EMkKxj7TJ4xo_2zX10-kEUfRlc_QUJHtgeMp5AyFAUZ7qprD33C-_8-rT5Wy-3itV5vXt4W83W955znWjTKSk4FUMPRcmi9Mk7P2KxF01itW5BWaOGdMVpTZ1ptLJNNQR4da72YVg-XvecYPkdMeXcMYxzKyR2Xs0Yo3jBZ1ONF7aHHXTf48PtlKYenzoYBfVfyuVZUCMoZEz_tOGJu</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2489352914</pqid></control><display><type>conference_proceeding</type><title>Labeling Modes of Operation and Extracting Features for Fault Detection with Cloud-Based Thermostat Data</title><source>ASHRAE Publications</source><creator>Rogers, Austin ; Guo, Fangzhou ; Ma ; Rasmussen, Bryan</creator><creatorcontrib>Rogers, Austin ; Guo, Fangzhou ; Ma ; Rasmussen, Bryan</creatorcontrib><description>This paper presents a method for transforming raw cloud-based thermostat data for cycling systems into a set of operating modes that is useful for large scale data analysis. Thermostat data typically includes the setpoint temperatures, the actual indoor temperature and the operating mode. These raw thermostat operating modes include "cooling on' "heating on', and "system off. The transformed operating modes include regulating modes, tracking modes, and free response modes. These new modes, which can be generated during data preprocessing, are used to more clearly show key system performance metrics and identify change points in the time series thermostat data. This paper includes the filtering logic used to label the operating modes and examples of insightful behavior that has been captured using this preprocessing method.</description><identifier>ISSN: 0001-2505</identifier><language>eng</language><publisher>Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE)</publisher><subject>Air conditioning ; Algorithms ; Analysis ; Control theory ; Data analysis ; Energy consumption ; Fault detection ; Feature extraction ; Information management ; Methods ; Performance measurement ; Preprocessing</subject><ispartof>ASHRAE transactions, 2020, Vol.126 (2), p.71-79</ispartof><rights>COPYRIGHT 2020 American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE)</rights><rights>Copyright American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc. 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782</link.rule.ids></links><search><creatorcontrib>Rogers, Austin</creatorcontrib><creatorcontrib>Guo, Fangzhou</creatorcontrib><creatorcontrib>Ma</creatorcontrib><creatorcontrib>Rasmussen, Bryan</creatorcontrib><title>Labeling Modes of Operation and Extracting Features for Fault Detection with Cloud-Based Thermostat Data</title><title>ASHRAE transactions</title><description>This paper presents a method for transforming raw cloud-based thermostat data for cycling systems into a set of operating modes that is useful for large scale data analysis. Thermostat data typically includes the setpoint temperatures, the actual indoor temperature and the operating mode. These raw thermostat operating modes include "cooling on' "heating on', and "system off. The transformed operating modes include regulating modes, tracking modes, and free response modes. These new modes, which can be generated during data preprocessing, are used to more clearly show key system performance metrics and identify change points in the time series thermostat data. This paper includes the filtering logic used to label the operating modes and examples of insightful behavior that has been captured using this preprocessing method.</description><subject>Air conditioning</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Control theory</subject><subject>Data analysis</subject><subject>Energy consumption</subject><subject>Fault detection</subject><subject>Feature extraction</subject><subject>Information management</subject><subject>Methods</subject><subject>Performance measurement</subject><subject>Preprocessing</subject><issn>0001-2505</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotz01PAjEQBuA9aCKi_6GJ5zX9LntEBDXBcMEzmW2nsGTZYtuN_nyrmDlM5s2TmcxVNaGUsporqm6q25SOZZJK6kl1WEOLfTfsyXtwmEjwZHPGCLkLA4HBkeV3jmDzr1gh5DEW5EMkKxj7TJ4xo_2zX10-kEUfRlc_QUJHtgeMp5AyFAUZ7qprD33C-_8-rT5Wy-3itV5vXt4W83W955znWjTKSk4FUMPRcmi9Mk7P2KxF01itW5BWaOGdMVpTZ1ptLJNNQR4da72YVg-XvecYPkdMeXcMYxzKyR2Xs0Yo3jBZ1ONF7aHHXTf48PtlKYenzoYBfVfyuVZUCMoZEz_tOGJu</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Rogers, Austin</creator><creator>Guo, Fangzhou</creator><creator>Ma</creator><creator>Rasmussen, Bryan</creator><general>American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE)</general><general>American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc</general><scope>3V.</scope><scope>7RQ</scope><scope>7TB</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0X</scope><scope>U9A</scope></search><sort><creationdate>20200101</creationdate><title>Labeling Modes of Operation and Extracting Features for Fault Detection with Cloud-Based Thermostat Data</title><author>Rogers, Austin ; Guo, Fangzhou ; Ma ; Rasmussen, Bryan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g222t-395c4203a072ec2abf57d6818be79c66ba4c363fd77660d7b67c14957dfed1bf3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Air conditioning</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Control theory</topic><topic>Data analysis</topic><topic>Energy consumption</topic><topic>Fault detection</topic><topic>Feature extraction</topic><topic>Information management</topic><topic>Methods</topic><topic>Performance measurement</topic><topic>Preprocessing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rogers, Austin</creatorcontrib><creatorcontrib>Guo, Fangzhou</creatorcontrib><creatorcontrib>Ma</creatorcontrib><creatorcontrib>Rasmussen, Bryan</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>Career & Technical Education Database</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rogers, Austin</au><au>Guo, Fangzhou</au><au>Ma</au><au>Rasmussen, Bryan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Labeling Modes of Operation and Extracting Features for Fault Detection with Cloud-Based Thermostat Data</atitle><btitle>ASHRAE transactions</btitle><date>2020-01-01</date><risdate>2020</risdate><volume>126</volume><issue>2</issue><spage>71</spage><epage>79</epage><pages>71-79</pages><issn>0001-2505</issn><abstract>This paper presents a method for transforming raw cloud-based thermostat data for cycling systems into a set of operating modes that is useful for large scale data analysis. Thermostat data typically includes the setpoint temperatures, the actual indoor temperature and the operating mode. These raw thermostat operating modes include "cooling on' "heating on', and "system off. The transformed operating modes include regulating modes, tracking modes, and free response modes. These new modes, which can be generated during data preprocessing, are used to more clearly show key system performance metrics and identify change points in the time series thermostat data. This paper includes the filtering logic used to label the operating modes and examples of insightful behavior that has been captured using this preprocessing method.</abstract><cop>Atlanta</cop><pub>American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE)</pub><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0001-2505 |
| ispartof | ASHRAE transactions, 2020, Vol.126 (2), p.71-79 |
| issn | 0001-2505 |
| language | eng |
| recordid | cdi_proquest_journals_2489352914 |
| source | ASHRAE Publications |
| subjects | Air conditioning Algorithms Analysis Control theory Data analysis Energy consumption Fault detection Feature extraction Information management Methods Performance measurement Preprocessing |
| title | Labeling Modes of Operation and Extracting Features for Fault Detection with Cloud-Based Thermostat Data |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T23%3A57%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Labeling%20Modes%20of%20Operation%20and%20Extracting%20Features%20for%20Fault%20Detection%20with%20Cloud-Based%20Thermostat%20Data&rft.btitle=ASHRAE%20transactions&rft.au=Rogers,%20Austin&rft.date=2020-01-01&rft.volume=126&rft.issue=2&rft.spage=71&rft.epage=79&rft.pages=71-79&rft.issn=0001-2505&rft_id=info:doi/&rft_dat=%3Cgale_proqu%3EA650330211%3C/gale_proqu%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-g222t-395c4203a072ec2abf57d6818be79c66ba4c363fd77660d7b67c14957dfed1bf3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2489352914&rft_id=info:pmid/&rft_galeid=A650330211&rfr_iscdi=true |