Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting

Pedro M. R. Bento, Jose A. N. Pombo, Maria R. A. Calado and Silvio J. P. S. Mariano
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Pedro M. R. Bento: Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
Jose A. N. Pombo: Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
Maria R. A. Calado: Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
Silvio J. P. S. Mariano: Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal

Energies, 2021, vol. 14, issue 21, 1-21

Abstract: Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

Keywords: ARIMA models; correlation analysis; deep learning; deep neural networks; ensemble methods; ISO New England; load forecasting; short-term load forecasting (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: 2021
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