Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city
The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining t...
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Veröffentlicht in: | Building research and information : the international journal of research, development and demonstration development and demonstration, 2021-01, Vol.49 (1), p.127-143 |
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creator | V E, Sathishkumar Shin, Changsun Cho, Yongyun |
description | The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types. Five statistical algorithms are used for energy consumption prediction:(a) General linear regression, (b) Classification and regression trees, (c) Support vector machine with a radial basis kernel, (d) K nearest neighbours, (e) CUBIST. Root mean squared error, Mean absolute error and Coefficient of variation are used to measure the prediction efficiency of the models. The results show that CUBIST model provides best results with lower error values and this model can be used for the development of energy efficient structural design which helps to optimize the energy consumption and policy making in smart cities. |
doi_str_mv | 10.1080/09613218.2020.1809983 |
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This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types. Five statistical algorithms are used for energy consumption prediction:(a) General linear regression, (b) Classification and regression trees, (c) Support vector machine with a radial basis kernel, (d) K nearest neighbours, (e) CUBIST. Root mean squared error, Mean absolute error and Coefficient of variation are used to measure the prediction efficiency of the models. The results show that CUBIST model provides best results with lower error values and this model can be used for the development of energy efficient structural design which helps to optimize the energy consumption and policy making in smart cities.</description><identifier>ISSN: 0961-3218</identifier><identifier>EISSN: 1466-4321</identifier><identifier>DOI: 10.1080/09613218.2020.1809983</identifier><language>eng</language><publisher>London: Routledge</publisher><subject>Algorithms ; Carbon dioxide ; Carbon dioxide emissions ; Coefficient of variation ; data analysis ; Data mining ; Design optimization ; Energy consumption ; Energy efficiency ; Energy management ; feature ranking ; Iron and steel industry ; Power factor ; Prediction models ; Reactive power ; Regression analysis ; Smart cities ; Statistical analysis ; Steel industry ; Structural design ; Structural engineering ; Support vector machines</subject><ispartof>Building research and information : the international journal of research, development and demonstration, 2021-01, Vol.49 (1), p.127-143</ispartof><rights>2020 Informa UK Limited, trading as Taylor & Francis Group 2020</rights><rights>2020 Informa UK Limited, trading as Taylor & Francis Group</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-f8393be0f60744fedf5c0f39bd6935c63e51aa3c5fa9e29840052b260447e1763</citedby><cites>FETCH-LOGICAL-c338t-f8393be0f60744fedf5c0f39bd6935c63e51aa3c5fa9e29840052b260447e1763</cites><orcidid>0000-0002-4855-4163 ; 0000-0002-8271-2022 ; 0000-0002-5494-4395</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>V E, Sathishkumar</creatorcontrib><creatorcontrib>Shin, Changsun</creatorcontrib><creatorcontrib>Cho, Yongyun</creatorcontrib><title>Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city</title><title>Building research and information : the international journal of research, development and demonstration</title><description>The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types. Five statistical algorithms are used for energy consumption prediction:(a) General linear regression, (b) Classification and regression trees, (c) Support vector machine with a radial basis kernel, (d) K nearest neighbours, (e) CUBIST. Root mean squared error, Mean absolute error and Coefficient of variation are used to measure the prediction efficiency of the models. The results show that CUBIST model provides best results with lower error values and this model can be used for the development of energy efficient structural design which helps to optimize the energy consumption and policy making in smart cities.</description><subject>Algorithms</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide emissions</subject><subject>Coefficient of variation</subject><subject>data analysis</subject><subject>Data mining</subject><subject>Design optimization</subject><subject>Energy consumption</subject><subject>Energy efficiency</subject><subject>Energy management</subject><subject>feature ranking</subject><subject>Iron and steel industry</subject><subject>Power factor</subject><subject>Prediction models</subject><subject>Reactive power</subject><subject>Regression analysis</subject><subject>Smart cities</subject><subject>Statistical analysis</subject><subject>Steel industry</subject><subject>Structural design</subject><subject>Structural engineering</subject><subject>Support vector machines</subject><issn>0961-3218</issn><issn>1466-4321</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEQgIMoWKs_QQh43jq7eXT3ppT6AMGLnkM2D0ndJjXJIvvvTW29epph5pth5kPouoZFDS3cQsdr0tTtooGmlFroupacoFlNOa9o6Zyi2Z6p9tA5ukhpAwANo2yGPtfWOuWMz9h4Ez8mrIJP43aXXfB4F4126jfdBm0GbEPEEmuZJZZeDlN2qjJe9oPR2Hk9phwn3I9u0M5_lEqB01bGjJXL0yU6s3JI5uoY5-j9Yf22eqpeXh-fV_cvlSKkzZVtSUd6A5bDklJrtGUKLOl6zTvCFCeG1VISxazsTNO1FIA1fcOB0qWpl5zM0c1h7y6Gr9GkLDZhjOXcJBq6ZMB5eb1Q7ECpGFKKxopddOXWSdQg9l7Fn1ex9yqOXsvc3WHO-WJjK79DHLTIchpCtFF65ZIg_6_4AcXDf-E</recordid><startdate>20210102</startdate><enddate>20210102</enddate><creator>V E, Sathishkumar</creator><creator>Shin, Changsun</creator><creator>Cho, Yongyun</creator><general>Routledge</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-4855-4163</orcidid><orcidid>https://orcid.org/0000-0002-8271-2022</orcidid><orcidid>https://orcid.org/0000-0002-5494-4395</orcidid></search><sort><creationdate>20210102</creationdate><title>Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city</title><author>V E, Sathishkumar ; Shin, Changsun ; Cho, Yongyun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-f8393be0f60744fedf5c0f39bd6935c63e51aa3c5fa9e29840052b260447e1763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Carbon dioxide</topic><topic>Carbon dioxide emissions</topic><topic>Coefficient of variation</topic><topic>data analysis</topic><topic>Data mining</topic><topic>Design optimization</topic><topic>Energy consumption</topic><topic>Energy efficiency</topic><topic>Energy management</topic><topic>feature ranking</topic><topic>Iron and steel industry</topic><topic>Power factor</topic><topic>Prediction models</topic><topic>Reactive power</topic><topic>Regression analysis</topic><topic>Smart cities</topic><topic>Statistical analysis</topic><topic>Steel industry</topic><topic>Structural design</topic><topic>Structural engineering</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>V E, Sathishkumar</creatorcontrib><creatorcontrib>Shin, Changsun</creatorcontrib><creatorcontrib>Cho, Yongyun</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Building research and information : the international journal of research, development and demonstration</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>V E, Sathishkumar</au><au>Shin, Changsun</au><au>Cho, Yongyun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city</atitle><jtitle>Building research and information : the international journal of research, development and demonstration</jtitle><date>2021-01-02</date><risdate>2021</risdate><volume>49</volume><issue>1</issue><spage>127</spage><epage>143</epage><pages>127-143</pages><issn>0961-3218</issn><eissn>1466-4321</eissn><abstract>The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types. Five statistical algorithms are used for energy consumption prediction:(a) General linear regression, (b) Classification and regression trees, (c) Support vector machine with a radial basis kernel, (d) K nearest neighbours, (e) CUBIST. Root mean squared error, Mean absolute error and Coefficient of variation are used to measure the prediction efficiency of the models. 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subjects | Algorithms Carbon dioxide Carbon dioxide emissions Coefficient of variation data analysis Data mining Design optimization Energy consumption Energy efficiency Energy management feature ranking Iron and steel industry Power factor Prediction models Reactive power Regression analysis Smart cities Statistical analysis Steel industry Structural design Structural engineering Support vector machines |
title | Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city |
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