Hybrid modeling in the predictive analytics of energy systems and prices

Emrah Gulay and Okan Duru

Applied Energy, 2020, vol. 268, issue C, No S0306261920304979

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.

Keywords: Energy markets; Energy prices; Price discovery; Price forecasting; Combination forecasting; Residual modeling (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920304979
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:268:y:2020:i:c:s0306261920304979

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.114985

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().