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.authorGallagher, Colm V.
dc.contributor.authorBruton, Ken
dc.contributor.authorO'Sullivan, Dominic T. J.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderNTR Foundation, Ireland
dc.date.accessioned2017-02-02T10:00:04Z
dc.date.available2017-02-02T10:00:04Z
dc.date.issued2016-06-14
dc.date.updated2017-02-02T09:43:15Z
dc.description.abstractThis 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.statusPeer revieweden
dc.description.urihttp://dmbd2016.ic-si.org/en
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGallagher 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_5en
dc.identifier.doi10.1007/978-3-319-40973-3_5
dc.identifier.endpage58en
dc.identifier.issn0302-9743
dc.identifier.startpage48en
dc.identifier.urihttps://hdl.handle.net/10468/3551
dc.language.isoenen
dc.publisherSpringer International Publishing AGen
dc.relation.ispartofData Mining and Big Data: First International Conference, DMBD 2016
dc.relation.ispartofLecture Notes in Computer Science 9714
dc.relation.ispartofseriesLecture Notes in Computer Science;9714
dc.relation.urihttp://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_5en
dc.subjectMeasurement and Verificationen
dc.subjectData miningen
dc.subjectEnergy efficiencyen
dc.subjectBaseline energy modellingen
dc.titleUtilising the Cross Industry Standard Process for Data Mining to reduce uncertainty in the Measurement and Verification of energy savingsen
dc.typeConference itemen
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