Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration
Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, th...
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| Veröffentlicht in: | Building and Environment 2014-09, Vol.79, p.1-12 |
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| creator | Sun, Kaiyu Yan, Da Hong, Tianzhen Guo, Siyue |
| description | Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.
•Overtime is common, stochastic, and varies by individual and time.•A new stochastic overtime model is developed using measured occupancy data.•The stochastic model is validated using measured cooling energy use data.•A new hybrid approach to energy model calibration is proposed.•The overtime model and hybrid calibration approach can improve simulation accuracy. |
| doi_str_mv | 10.1016/j.buildenv.2014.04.030 |
| format | Article |
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•Overtime is common, stochastic, and varies by individual and time.•A new stochastic overtime model is developed using measured occupancy data.•The stochastic model is validated using measured cooling energy use data.•A new hybrid approach to energy model calibration is proposed.•The overtime model and hybrid calibration approach can improve simulation accuracy.</description><identifier>ISSN: 0360-1323</identifier><identifier>EISSN: 1873-684X</identifier><identifier>DOI: 10.1016/j.buildenv.2014.04.030</identifier><identifier>CODEN: BUENDB</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Applied sciences ; Building energy use ; building energy use, building simulation, model calibration, occupant behavior, overtime occupancy, stochastic modeling ; Building simulation ; Building technical equipments ; Buildings ; Buildings. Public works ; Calibration ; Computation methods. Tables. Charts ; Computer simulation ; Construction ; ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION ; Energy management and energy conservation in building ; Energy use ; Environmental engineering ; Exact sciences and technology ; Model calibration ; Occupant behavior ; Office buildings ; Overtime occupancy ; Stochastic modeling ; Stochasticity ; Structural analysis. Stresses ; Working hours</subject><ispartof>Building and Environment, 2014-09, Vol.79, p.1-12</ispartof><rights>2014 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c483t-f1c96f4adee96bf834c6c7d0ce9a39a61a5f6d56fb13de5440f00c3c7364c0bf3</citedby><cites>FETCH-LOGICAL-c483t-f1c96f4adee96bf834c6c7d0ce9a39a61a5f6d56fb13de5440f00c3c7364c0bf3</cites><orcidid>0000-0003-1886-9137</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0360132314001346$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28600662$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1164209$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Sun, Kaiyu</creatorcontrib><creatorcontrib>Yan, Da</creatorcontrib><creatorcontrib>Hong, Tianzhen</creatorcontrib><creatorcontrib>Guo, Siyue</creatorcontrib><creatorcontrib>Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)</creatorcontrib><title>Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration</title><title>Building and Environment</title><description>Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.
•Overtime is common, stochastic, and varies by individual and time.•A new stochastic overtime model is developed using measured occupancy data.•The stochastic model is validated using measured cooling energy use data.•A new hybrid approach to energy model calibration is proposed.•The overtime model and hybrid calibration approach can improve simulation accuracy.</description><subject>Applied sciences</subject><subject>Building energy use</subject><subject>building energy use, building simulation, model calibration, occupant behavior, overtime occupancy, stochastic modeling</subject><subject>Building simulation</subject><subject>Building technical equipments</subject><subject>Buildings</subject><subject>Buildings. Public works</subject><subject>Calibration</subject><subject>Computation methods. Tables. Charts</subject><subject>Computer simulation</subject><subject>Construction</subject><subject>ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION</subject><subject>Energy management and energy conservation in building</subject><subject>Energy use</subject><subject>Environmental engineering</subject><subject>Exact sciences and technology</subject><subject>Model calibration</subject><subject>Occupant behavior</subject><subject>Office buildings</subject><subject>Overtime occupancy</subject><subject>Stochastic modeling</subject><subject>Stochasticity</subject><subject>Structural analysis. Stresses</subject><subject>Working hours</subject><issn>0360-1323</issn><issn>1873-684X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkVGL1DAUhYsoOK7-BQmC4EvHmyaTtm_KsurCgg8q-BbS25vdjG1Sk3Zg_r3pdvV1hQsh5Du5nHOK4jWHPQeu3h_33eKGnvxpXwGXe8gj4Emx400tStXIn0-LHQgFJReVeF68SOkIWdgKuSt-fZsD3pk0O2Rj6Glw_pYFy8KJ4uxGYgFxmYzHMzO-Z25OzEzT4NDMLnjmPLtfvqrIU7w9s-TGZdheVwWawXXx_v6yeGbNkOjVw3lR_Ph09f3yS3nz9fP15cebEmUj5tJybJWVpidqVWcbIVFh3QNSa0RrFDcHq_qDsh0XPR2kBAuAAmuhJEJnxUXxZvs3ZFs6oZsJ7zB4TzhrzpWsoM3Quw2aYvi9UJr16BLSMBhPYUl6xSrFQcj_QKu6rRtxUBlVG4oxpBTJ6im60cSz5qDXtvRR_21Lr21pyCMgC98-7DApR2Zjjtylf-qqUQBKVZn7sHGUAzw5iqs_8ki9i6u9PrjHVv0Bk7qwUA</recordid><startdate>20140901</startdate><enddate>20140901</enddate><creator>Sun, Kaiyu</creator><creator>Yan, Da</creator><creator>Hong, Tianzhen</creator><creator>Guo, Siyue</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7SU</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>OIOZB</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000-0003-1886-9137</orcidid></search><sort><creationdate>20140901</creationdate><title>Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration</title><author>Sun, Kaiyu ; Yan, Da ; Hong, Tianzhen ; Guo, Siyue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c483t-f1c96f4adee96bf834c6c7d0ce9a39a61a5f6d56fb13de5440f00c3c7364c0bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Building energy use</topic><topic>building energy use, building simulation, model calibration, occupant behavior, overtime occupancy, stochastic modeling</topic><topic>Building simulation</topic><topic>Building technical equipments</topic><topic>Buildings</topic><topic>Buildings. Public works</topic><topic>Calibration</topic><topic>Computation methods. Tables. Charts</topic><topic>Computer simulation</topic><topic>Construction</topic><topic>ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION</topic><topic>Energy management and energy conservation in building</topic><topic>Energy use</topic><topic>Environmental engineering</topic><topic>Exact sciences and technology</topic><topic>Model calibration</topic><topic>Occupant behavior</topic><topic>Office buildings</topic><topic>Overtime occupancy</topic><topic>Stochastic modeling</topic><topic>Stochasticity</topic><topic>Structural analysis. Stresses</topic><topic>Working hours</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Kaiyu</creatorcontrib><creatorcontrib>Yan, Da</creatorcontrib><creatorcontrib>Hong, Tianzhen</creatorcontrib><creatorcontrib>Guo, Siyue</creatorcontrib><creatorcontrib>Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><jtitle>Building and Environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Kaiyu</au><au>Yan, Da</au><au>Hong, Tianzhen</au><au>Guo, Siyue</au><aucorp>Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration</atitle><jtitle>Building and Environment</jtitle><date>2014-09-01</date><risdate>2014</risdate><volume>79</volume><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>0360-1323</issn><eissn>1873-684X</eissn><coden>BUENDB</coden><abstract>Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.
•Overtime is common, stochastic, and varies by individual and time.•A new stochastic overtime model is developed using measured occupancy data.•The stochastic model is validated using measured cooling energy use data.•A new hybrid approach to energy model calibration is proposed.•The overtime model and hybrid calibration approach can improve simulation accuracy.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.buildenv.2014.04.030</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-1886-9137</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Elsevier ScienceDirect Journals |
| subjects | Applied sciences Building energy use building energy use, building simulation, model calibration, occupant behavior, overtime occupancy, stochastic modeling Building simulation Building technical equipments Buildings Buildings. Public works Calibration Computation methods. Tables. Charts Computer simulation Construction ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION Energy management and energy conservation in building Energy use Environmental engineering Exact sciences and technology Model calibration Occupant behavior Office buildings Overtime occupancy Stochastic modeling Stochasticity Structural analysis. Stresses Working hours |
| title | Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration |
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