From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings

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dc.contributor.authorGallagher, Colm V.
dc.contributor.authorO'Donovan, Peter
dc.contributor.authorLeahy, Kevin
dc.contributor.authorBruton, Ken
dc.contributor.authorO'Sullivan, Dominic T. J.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-01-21T15:26:48Z
dc.date.available2019-01-21T15:26:48Z
dc.date.issued2018-09
dc.date.updated2019-01-21T15:20:42Z
dc.description.abstractThe European Union's Energy Efficiency Directive is placing an increased focus on the measurement and verification (M&V) of demand side energy savings. The objective of M&V is to quantify energy savings with minimum uncertainty. M&V is currently undergoing a transition to practices, known as M&V 2.0, that employ automated advanced analytics to verify performance. This offers the opportunity to effectively manage the transition from short-term M&V to long-term monitoring and targeting (M&T) in industrial facilities. The original contribution of this paper consists of a novel, robust and technology agnostic framework that not only satisfies the requirements of M&V 2.0, but also bridges the gap between M&V and M&T by ensuring persistence of savings. The approach features a unique machine learning-based energy modelling methodology, model deployment and an exception reporting system that ensures early identification of performance degradation. A case study demonstrates the effectiveness of the approach. Savings from a real-world project are found to be 177,962 +/- 12,334 kWh with a 90% confidence interval. The uncertainty associated with the savings is 8.6% of the allowable uncertainty, thus highlighting the viability of the framework as a reliable and effective tool.en
dc.description.statusPeer revieweden
dc.description.urihttps://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8476665en
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGallagher, C. V., O’Donovan, P., Leahy, K., Bruton, K. and O’Sullivan, D. T. J. (2018) 'From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings'. 2018 International Conference on Smart Energy Systems and Technologies (SEST), Sevilla, Spain, 10-12 September. doi: 10.1109/SEST.2018.8495711en
dc.identifier.doi10.1109/SEST.2018.8495711
dc.identifier.endpage6en
dc.identifier.isbn978-1-5386-5326-5
dc.identifier.journaltitle2018 International Conference on Smart Energy Systems and Technologies (SESTen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/7334
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2018 International Conference on Smart Energy Systems and Technologies (SEST)
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2302/IE/Marine Renewable Energy Ireland (MaREI) - The SFI Centre for Marine Renewable Energy Research/en
dc.relation.urihttps://ieeexplore.ieee.org/document/8495711
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectDemand side managementen
dc.subjectEnergy conservationen
dc.subjectLearning (artificial intelligence)en
dc.subjectProduction facilitiesen
dc.subjectArtificial intelligence-based frameworken
dc.subjectReal-time performance verificationen
dc.subjectEnergy savingsen
dc.subjectLong-term monitoringen
dc.subjectTechnology agnostic frameworken
dc.subjectMachine learning-based energy modelling methodologyen
dc.subjectAutomated advanced analyticsen
dc.subjectEuropean Union Energy Efficiency Directiveen
dc.subjectM&Ven
dc.subjectM&Ten
dc.subjectDemand-side energy savingsen
dc.subjectMeasurement and verificationen
dc.subjectMonitoring and targetingen
dc.subjectIndustrial facilitiesen
dc.subjectEnergy 12334.0 kWhen
dc.subjectMathematical modelen
dc.subjectUncertaintyen
dc.subjectMachine learningen
dc.subjectComputational modelingen
dc.subjectBuildingsen
dc.subjectData modelsen
dc.subjectEnergy measurementen
dc.subjectPerformance verificationen
dc.subjectEnergy efficiencyen
dc.subjectM&V 2.0en
dc.subjectEnergy modellingen
dc.titleFrom M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savingsen
dc.typeConference itemen
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