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Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus
•A multiobjective renewable energy facility allocation model (MOFAM) was built.•MOFAM provides multi-choice Pareto allocations of the facilities on campus.•Allocation is a trade-off problem among cost, investment return, and CO2 reduction.•MOFAM estimated 4 million invested for 509t CO2 reduction an...
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Published in: | Energy and buildings 2016-06, Vol.122, p.120-130 |
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creator | Yang, Ming-Der Chen, Yi-Ping Lin, Yu-Hao Ho, Yu-Feng Lin, Ji-Yuan |
description | •A multiobjective renewable energy facility allocation model (MOFAM) was built.•MOFAM provides multi-choice Pareto allocations of the facilities on campus.•Allocation is a trade-off problem among cost, investment return, and CO2 reduction.•MOFAM estimated 4 million invested for 509t CO2 reduction and 2 million in return.
For energy conservation and CO2 emission reduction, renewable energy facilities, such as solar equipments and rooftop gardens, are considered effective for energy management of institutional buildings in a community. This study integrated an energy mixture facility model with a nondominated sorting genetic algorithm-II optimizer as a multiobjective optimal facility allocation model (MOFAM) for allocating renewable energy facilities on the rooftop of campus buildings.
A case study was conducted on a college campus to demonstrate the feasibility of MOFAM. MOFAM offers simple steps and provides more allocation plans to satisfy decision-makers’ requirements for minimum investment cost, maximum CO2 reduction, and maximum investment returns. In addition, the result demonstrates that the multiobjective optimal model considering three objectives resulted in optimal solutions that include the optimal solutions generated from two-objective optimization. In this campus case, MOFAM helped decision-makers optimize the installation area of solar photovoltaic panels, the installation area of solar water heaters, and the area of rooftop gardens on campus rooftops to perform effective management for institutional buildings for conserving energy and CO2 reduction. |
doi_str_mv | 10.1016/j.enbuild.2016.04.027 |
format | article |
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For energy conservation and CO2 emission reduction, renewable energy facilities, such as solar equipments and rooftop gardens, are considered effective for energy management of institutional buildings in a community. This study integrated an energy mixture facility model with a nondominated sorting genetic algorithm-II optimizer as a multiobjective optimal facility allocation model (MOFAM) for allocating renewable energy facilities on the rooftop of campus buildings.
A case study was conducted on a college campus to demonstrate the feasibility of MOFAM. MOFAM offers simple steps and provides more allocation plans to satisfy decision-makers’ requirements for minimum investment cost, maximum CO2 reduction, and maximum investment returns. In addition, the result demonstrates that the multiobjective optimal model considering three objectives resulted in optimal solutions that include the optimal solutions generated from two-objective optimization. In this campus case, MOFAM helped decision-makers optimize the installation area of solar photovoltaic panels, the installation area of solar water heaters, and the area of rooftop gardens on campus rooftops to perform effective management for institutional buildings for conserving energy and CO2 reduction.</description><identifier>ISSN: 0378-7788</identifier><identifier>DOI: 10.1016/j.enbuild.2016.04.027</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Algorithms ; Allocations ; Campus ; Carbon dioxide ; CO2 reduction ; Energy conservation ; Multiobjective optimization ; Nondominated sorting genetic algorithm (NSGA-II) ; Optimization ; Reduction ; Renewable energy ; Sorting</subject><ispartof>Energy and buildings, 2016-06, Vol.122, p.120-130</ispartof><rights>2016 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-49f25b7865bcbc1c4ac2c7b88e9fce223893d7d29bfdaf8e6268f37ee734be533</citedby><cites>FETCH-LOGICAL-c375t-49f25b7865bcbc1c4ac2c7b88e9fce223893d7d29bfdaf8e6268f37ee734be533</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids></links><search><creatorcontrib>Yang, Ming-Der</creatorcontrib><creatorcontrib>Chen, Yi-Ping</creatorcontrib><creatorcontrib>Lin, Yu-Hao</creatorcontrib><creatorcontrib>Ho, Yu-Feng</creatorcontrib><creatorcontrib>Lin, Ji-Yuan</creatorcontrib><title>Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus</title><title>Energy and buildings</title><description>•A multiobjective renewable energy facility allocation model (MOFAM) was built.•MOFAM provides multi-choice Pareto allocations of the facilities on campus.•Allocation is a trade-off problem among cost, investment return, and CO2 reduction.•MOFAM estimated 4 million invested for 509t CO2 reduction and 2 million in return.
For energy conservation and CO2 emission reduction, renewable energy facilities, such as solar equipments and rooftop gardens, are considered effective for energy management of institutional buildings in a community. This study integrated an energy mixture facility model with a nondominated sorting genetic algorithm-II optimizer as a multiobjective optimal facility allocation model (MOFAM) for allocating renewable energy facilities on the rooftop of campus buildings.
A case study was conducted on a college campus to demonstrate the feasibility of MOFAM. MOFAM offers simple steps and provides more allocation plans to satisfy decision-makers’ requirements for minimum investment cost, maximum CO2 reduction, and maximum investment returns. In addition, the result demonstrates that the multiobjective optimal model considering three objectives resulted in optimal solutions that include the optimal solutions generated from two-objective optimization. In this campus case, MOFAM helped decision-makers optimize the installation area of solar photovoltaic panels, the installation area of solar water heaters, and the area of rooftop gardens on campus rooftops to perform effective management for institutional buildings for conserving energy and CO2 reduction.</description><subject>Algorithms</subject><subject>Allocations</subject><subject>Campus</subject><subject>Carbon dioxide</subject><subject>CO2 reduction</subject><subject>Energy conservation</subject><subject>Multiobjective optimization</subject><subject>Nondominated sorting genetic algorithm (NSGA-II)</subject><subject>Optimization</subject><subject>Reduction</subject><subject>Renewable energy</subject><subject>Sorting</subject><issn>0378-7788</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqNkcuO1DAQRbMAiWHgE5C8ZJPg2EnsrBAa8WhpEBtYW36Um2oldmM7jYZ_4V9xK8MaVqW6Prck6zTNq552Pe2nN6cOgtlwcR2ra0eHjjLxpLmhXMhWCCmfNc9zPlFKp1H0N83vz9tSMJoT2IIXIPFccMVfumaBbBnDkYQYXFwx6AKO5JjKNTxCgIKW6OUYE5bva3s4EB9TDZZo93r0pFLp-EBsDBnSZY91cCTVh5_aLPCX8NriggUhE6wIsXo9b_lF89TrJcPLx3nbfPvw_uvdp_b-y8fD3bv71nIxlnaYPRuNkNNorLG9HbRlVhgpYfYWGONy5k44NhvvtJcwsUl6LgAEHwyMnN82r_e75xR_bJCLWjFbWBYdIG5Z9bKfqOSs_x-UymkeJzFUdNxRm2LOCbw6J1x1elA9VVdb6qQebamrLUUHVW3V3tu9B_XLF4SkskUIFhymqkm5iP-48AfyoqgK</recordid><startdate>20160615</startdate><enddate>20160615</enddate><creator>Yang, Ming-Der</creator><creator>Chen, Yi-Ping</creator><creator>Lin, Yu-Hao</creator><creator>Ho, Yu-Feng</creator><creator>Lin, Ji-Yuan</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20160615</creationdate><title>Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus</title><author>Yang, Ming-Der ; Chen, Yi-Ping ; Lin, Yu-Hao ; Ho, Yu-Feng ; Lin, Ji-Yuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-49f25b7865bcbc1c4ac2c7b88e9fce223893d7d29bfdaf8e6268f37ee734be533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Allocations</topic><topic>Campus</topic><topic>Carbon dioxide</topic><topic>CO2 reduction</topic><topic>Energy conservation</topic><topic>Multiobjective optimization</topic><topic>Nondominated sorting genetic algorithm (NSGA-II)</topic><topic>Optimization</topic><topic>Reduction</topic><topic>Renewable energy</topic><topic>Sorting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Ming-Der</creatorcontrib><creatorcontrib>Chen, Yi-Ping</creatorcontrib><creatorcontrib>Lin, Yu-Hao</creatorcontrib><creatorcontrib>Ho, Yu-Feng</creatorcontrib><creatorcontrib>Lin, Ji-Yuan</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment 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><jtitle>Energy and buildings</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Ming-Der</au><au>Chen, Yi-Ping</au><au>Lin, Yu-Hao</au><au>Ho, Yu-Feng</au><au>Lin, Ji-Yuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus</atitle><jtitle>Energy and buildings</jtitle><date>2016-06-15</date><risdate>2016</risdate><volume>122</volume><spage>120</spage><epage>130</epage><pages>120-130</pages><issn>0378-7788</issn><abstract>•A multiobjective renewable energy facility allocation model (MOFAM) was built.•MOFAM provides multi-choice Pareto allocations of the facilities on campus.•Allocation is a trade-off problem among cost, investment return, and CO2 reduction.•MOFAM estimated 4 million invested for 509t CO2 reduction and 2 million in return.
For energy conservation and CO2 emission reduction, renewable energy facilities, such as solar equipments and rooftop gardens, are considered effective for energy management of institutional buildings in a community. This study integrated an energy mixture facility model with a nondominated sorting genetic algorithm-II optimizer as a multiobjective optimal facility allocation model (MOFAM) for allocating renewable energy facilities on the rooftop of campus buildings.
A case study was conducted on a college campus to demonstrate the feasibility of MOFAM. MOFAM offers simple steps and provides more allocation plans to satisfy decision-makers’ requirements for minimum investment cost, maximum CO2 reduction, and maximum investment returns. In addition, the result demonstrates that the multiobjective optimal model considering three objectives resulted in optimal solutions that include the optimal solutions generated from two-objective optimization. In this campus case, MOFAM helped decision-makers optimize the installation area of solar photovoltaic panels, the installation area of solar water heaters, and the area of rooftop gardens on campus rooftops to perform effective management for institutional buildings for conserving energy and CO2 reduction.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.enbuild.2016.04.027</doi><tpages>11</tpages></addata></record> |
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subjects | Algorithms Allocations Campus Carbon dioxide CO2 reduction Energy conservation Multiobjective optimization Nondominated sorting genetic algorithm (NSGA-II) Optimization Reduction Renewable energy Sorting |
title | Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus |
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