Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155500
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dc.contributor.authorGulay, Emrahen_US
dc.contributor.authorDuru, Okanen_US
dc.date.accessioned2022-03-02T08:44:50Z-
dc.date.available2022-03-02T08:44:50Z-
dc.date.issued2020-
dc.identifier.citationGulay, E. & Duru, O. (2020). Hybrid modeling in the predictive analytics of energy systems and prices. Applied Energy, 268, 114985-. https://dx.doi.org/10.1016/j.apenergy.2020.114985en_US
dc.identifier.issn0306-2619en_US
dc.identifier.urihttps://hdl.handle.net/10356/155500-
dc.description.abstractThe aim of this paper is to illustrate the nature of the residuals of a forecasting process and to propose a hybrid approach with linear and nonlinear components predicted by corresponding methodologies. It is a common practice that residuals are assumed to be unpredictable or are reiterated into a model as lagged variables to capture any information remaining in the residual data. The central argument of this paper is that residuals from energy price forecasting can still carry predictive information in its complex and nonlinear form. Although the linear modeling is initially very accurate, reiterating residuals in linear structures is a mismatch of data type and methodology. In this regard, the proposed algorithm hybridizes or combines linear components captured by the Autoregressive Distributed Lag Model (ARDL) and nonlinear components processed by the Empirical Mode Decomposition (EMD) and an Artificial Neural Network (ANN) to improve post-sample accuracy. The conventional reiterative process can improve in-sample accuracy, which literally has no value for business forecasting practices. Through a fair benchmark comparison, including methodologies of other combinations, the proposed algorithm is cross-validated by predictive accuracy gain in the out-of-sample (holdout) dataset.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation.ispartofApplied Energyen_US
dc.rights© 2020 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleHybrid modeling in the predictive analytics of energy systems and pricesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doi10.1016/j.apenergy.2020.114985-
dc.identifier.scopus2-s2.0-85084634617-
dc.identifier.volume268en_US
dc.identifier.spage114985en_US
dc.subject.keywordsPrice Discoveryen_US
dc.subject.keywordsEnergy Marketsen_US
dc.description.acknowledgementThis paper has been funded by the College of Engineering, Nanyang Technological University.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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