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

Khairalla, Mergani A.; Ning, Xu; AL-Jallad, Nashat T.; El-Faroug, Musaab O.

doi:10.3390/en11061605

Cited by 79Forecasting Techniques and ApplicationsEnergy Load and Power ForecastingStock Market Forecasting Methods

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

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Khairalla, Mergani A., Ning, Xu, AL-Jallad, Nashat T., El-Faroug, Musaab O.·2018·Energies

DOI 10.3390/en11061605

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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.

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Ensemble classifiers or regressors are often far more precise than discrete classifiers or regressors, as supported by reported accuracies in applications across sectors.
“In all these applications, their reported accuracies support the argument that ensemble classifiers or regressors are often far more precise than the discrete classifiers or regressors.”
From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [16]
Stacking seeks to increase the predictive power of a classifier.
“STK seeks to increase the predictive power of a classifier [16].”
From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [16]

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Authors: Khairalla, M. A., Ning, X., AL-Jallad, N. T., & El-Faroug, M. O.
Affiliations: Nile Valley University (SD)
School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
Wuhan University of Technology (CN)
University of El Imam El Mahdi (SD)
Corresponding author: Khairalla, Mergani A.
ORCID: Khairalla, Mergani A.
AL-Jallad, Nashat T.
El-Faroug, Musaab O.
Venue: Energies
Publisher: MDPI AG
Published: 2018-06-19
Volume: 11
Issue: 6
Pages: 1605
ISSN: 1996-1073 (ISSN-L)
DOI: 10.3390/en11061605
OpenAlex: W2808814577
Cited by: 79
References: 52
Citations by year: 2026: 3 · 2025: 6 · 2024: 4 · 2023: 13 · 2022: 13 · 2021: 15 · 2020: 14 · 2019: 8 · 2018: 3
FWCI: 7.99
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Energies (publishedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: China Scholarship Council
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Keywords: short-term forecasting, load forecasting, stacking ensemble, heterogeneous ensemble learning, oil consumption, support vector regression, BP neural network, linear regression, directional accuracy, nonlinear time series

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

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