Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model

Mergani A. Khairalla, Xu Ning, Nashat T. AL-Jallad and Musaab O. El-Faroug
Additional contact information
Mergani A. Khairalla: School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
Xu Ning: School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
Nashat T. AL-Jallad: School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
Musaab O. El-Faroug: Faculty of Engineering, Elimam Elmahdi University, Kosti 11588, Sudan

Energies, 2018, vol. 11, issue 6, 1-21

Abstract: In the real-life, time-series data comprise a complicated pattern, hence it may be challenging to increase prediction accuracy rates by using machine learning and conventional statistical methods as single learners. This research outlines and investigates the Stacking Multi-Learning Ensemble (SMLE) model for time series prediction problem over various horizons with a focus on the forecasts accuracy, directions hit-rate, and the average growth rate of total oil demand. This investigation presents a flexible ensemble framework in light of blend heterogeneous models for demonstrating and forecasting nonlinear time series. The proposed SMLE model combines support vector regression (SVR), backpropagation neural network (BPNN), and linear regression (LR) learners, the ensemble architecture consists of four phases: generation, pruning, integration, and ensemble prediction task. We have conducted an empirical study to evaluate and compare the performance of SMLE using Global Oil Consumption (GOC). Thus, the assessment of the proposed model was conducted at single and multistep horizon prediction using unique benchmark techniques. The final results reveal that the proposed SMLE model outperforms all the other benchmark methods listed in this study at various levels such as error rate, similarity, and directional accuracy by 0.74%, 0.020%, and 91.24%, respectively. Therefore, this study demonstrates that the ensemble model is an extremely encouraging methodology for complex time series forecasting.

Keywords: time series forecasting; ensemble learning; heterogeneous models; SMLE; oil consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/6/1605/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/6/1605/ (text/html)

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:gam:jeners:v:11:y:2018:i:6:p:1605-:d:153338

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().