Utilising the Cross Industry Standard Process for Data Mining to reduce uncertainty in the Measurement and Verification of energy savings

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dc.contributor.author Gallagher, Colm V.
dc.contributor.author Bruton, Ken
dc.contributor.author O'Sullivan, Dominic T. J.
dc.date.accessioned 2017-02-02T10:00:04Z
dc.date.available 2017-02-02T10:00:04Z
dc.date.issued 2016-06-14
dc.identifier.citation Gallagher C. V., Bruton K. and O’Sullivan D. T. J. (2016) ‘Utilising the Cross Industry Standard Process for Data Mining to reduce uncertainty in the Measurement and Verification of energy savings’, in Tan Y. and Shi Y. (eds.) Data Mining and Big Data - DMBD 2016, Bali, Indonesia, 25-30 June. Lecture Notes in Computer Science, Vol. 9714. Springer International Publishing AG. doi:10.1007/978-3-319-40973-3_5 en
dc.identifier.startpage 48 en
dc.identifier.endpage 58 en
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10468/3551
dc.identifier.doi 10.1007/978-3-319-40973-3_5
dc.description.abstract This paper investigates the application of Data Mining (DM) to predict baseline energy consumption for the improvement of energy savings estimation accuracy in Measurement and Verification (M&V). M&V is a requirement of a certified energy management system (EnMS). A critical stage of the M&V process is the normalisation of data post Energy Conservation Measure (ECM) to pre-ECM conditions. Traditional M&V approaches utilise simplistic modelling techniques, which dilute the power of the available data. DM enables the true power of the available energy data to be harnessed with complex modelling techniques. The methodology proposed incorporates DM into the M&V process to improve prediction accuracy. The application of multi-variate regression and artificial neural networks to predict compressed air energy consumption in a manufacturing facility is presented. Predictions made using DM were consistently more accurate than those found using traditional approaches when the training period was greater than two months. en
dc.description.uri http://dmbd2016.ic-si.org/ en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer International Publishing AG en
dc.relation.ispartof Data Mining and Big Data: First International Conference, DMBD 2016
dc.relation.ispartof Lecture Notes in Computer Science 9714
dc.relation.ispartofseries Lecture Notes in Computer Science;9714
dc.relation.uri http://link.springer.com/book/10.1007/978-3-319-40973-3
dc.rights © 2016, Springer International Publishing AG. The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-319-40973-3_5 en
dc.subject Measurement and Verification en
dc.subject Data mining en
dc.subject Energy efficiency en
dc.subject Baseline energy modelling en
dc.title Utilising the Cross Industry Standard Process for Data Mining to reduce uncertainty in the Measurement and Verification of energy savings en
dc.type Conference item en
dc.internal.authorcontactother Ken Bruton, School Of Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: k.bruton@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2017-02-02T09:43:15Z
dc.description.version Accepted Version en
dc.internal.rssid 365845591
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder NTR Foundation, Ireland
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Bali, Indonesia en
dc.internal.IRISemailaddress k.bruton@ucc.ie en
dc.internal.IRISemailaddress dominic.osullivan@ucc.ie en

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