Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155500
Title: Hybrid modeling in the predictive analytics of energy systems and prices
Authors: Gulay, Emrah
Duru, Okan
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Gulay, 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.2020.114985
Journal: Applied Energy
Abstract: The 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.
URI: https://hdl.handle.net/10356/155500
ISSN: 0306-2619
DOI: 10.1016/j.apenergy.2020.114985
Rights: © 2020 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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