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

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  • Mergani A. Khairalla

    (School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
    School of Science and Technology, Nile Valley University, Atbara 346, Sudan)

  • 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)

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.

Suggested Citation

  • Mergani A. Khairalla & Xu Ning & Nashat T. AL-Jallad & Musaab O. El-Faroug, 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model," Energies, MDPI, vol. 11(6), pages 1-21, June.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1605-:d:153338
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