Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon (), Laio Oriel Seman, Viviana Cocco Mariani and Leandro dos Santos Coelho
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Anne Carolina Rodrigues Klaar: Graduate Program in Education, University of Planalto Catarinense, Lages 88509-900, Brazil
Stefano Frizzo Stefenon: Digital Industry Center, Fondazione Bruno Kessler, 38123 Trento, Italy
Laio Oriel Seman: Graduate Program in Applied Computer Science, University of Vale do Itajai, Itajai 88302-901, Brazil
Viviana Cocco Mariani: Mechanical Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil
Leandro dos Santos Coelho: Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil

Energies, 2023, vol. 16, issue 7, 1-17

Abstract: The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10 − 9 in the testing phase.

Keywords: electricity spot prices; ensemble learning methods; Latin America; seasonal decomposition; time series 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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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