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

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

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)...

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Bibliographic Details
Main Authors: Mergani A. Khairalla, Xu Ning, Nashat T. AL-Jallad, Musaab O. El-Faroug
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Energies
Subjects:
time series forecasting
ensemble learning
heterogeneous models
SMLE
oil consumption
Online Access:http://www.mdpi.com/1996-1073/11/6/1605

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http://www.mdpi.com/1996-1073/11/6/1605

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