Hybrid modeling in the predictive analytics of energy systems and prices

Gulay E., Duru O.

APPLIED ENERGY, vol.268, 2020 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 268
  • Publication Date: 2020
  • Doi Number: 10.1016/j.apenergy.2020.114985
  • Journal Name: APPLIED ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Biotechnology Research Abstracts, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Energy markets, Energy prices, Price discovery, Price forecasting, Combination forecasting, Residual modeling, ARTIFICIAL NEURAL-NETWORK, FORECASTING TIME-SERIES, ELECTRICITY CONSUMPTION, WIND-SPEED, ANN MODEL, DEMAND, ARIMA, DECOMPOSITION, THAILAND, OUTPUT
  • Dokuz Eylül University Affiliated: Yes


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.