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dc.contributor.authorVanli, N.D.en_US
dc.contributor.authorSayin, M.O.en_US
dc.contributor.authorYildiz H.en_US
dc.contributor.authorGoze, T.en_US
dc.contributor.authorKozat, S.S.en_US
dc.date.accessioned2016-02-08T12:25:58Z
dc.date.available2016-02-08T12:25:58Z
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/11693/28641
dc.description.abstractWe study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, which can be subsequently used to optimize energy consumption. To this end, we use the energy consumption history of the users and define finite state (FS) predictors according to the relative ordering patterns of these past observations. In order to alleviate the overfitting problems, we generate equivalence classes by tying several states in a nested manner. Using the resulting equivalence classes, we obtain a doubly exponential number of different FS predictors, one among which achieves the smallest accumulated loss, hence is optimal for the prediction task. We then introduce an algorithm to achieve the performance of this FS predictor among all doubly exponential number of FS predictors with a significantly reduced computational complexity. Our approach is generic in the sense that different tying configurations and loss functions can be incorporated into our framework in a straightforward manner. We illustrate the merits of the proposed algorithm using the real life energy usage data. © 2014 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.title2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/GlobalSIP.2014.7032114en_US
dc.subjectOnline learningen_US
dc.subjectOrder preserving pattern matchingen_US
dc.subjectSequential predictionen_US
dc.subjectEquivalence classesen_US
dc.subjectForecastingen_US
dc.subjectInformation scienceen_US
dc.subjectPattern matchingen_US
dc.subjectExponential numbersen_US
dc.subjectLoss functionsen_US
dc.subjectOnline learningen_US
dc.subjectOrder preservingen_US
dc.subjectOver fitting problemen_US
dc.subjectPrediction tasksen_US
dc.subjectRelative orderen_US
dc.subjectSequential predictionen_US
dc.subjectEnergy utilizationen_US
dc.titleEnergy consumption forecasting via order preserving pattern matchingen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.spage238en_US
dc.citation.epage242en_US
dc.identifier.doi10.1109/GlobalSIP.2014.7032114en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US


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