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

Author

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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
    Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy)

  • Laio Oriel Seman

    (Graduate Program in Applied Computer Science, University of Vale do Itajai, Itajai 88302-901, Brazil
    Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil)

  • Viviana Cocco Mariani

    (Mechanical Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil
    Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil)

  • Leandro dos Santos Coelho

    (Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil
    Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil)

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.

Suggested Citation

  • Anne Carolina Rodrigues Klaar & Stefano Frizzo Stefenon & Laio Oriel Seman & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2023. "Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico," Energies, MDPI, vol. 16(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3184-:d:1113441
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    References listed on IDEAS

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    2. Tamás Orosz & Anton Rassõlkin & Pedro Arsénio & Peter Poór & Daniil Valme & Ádám Sleisz, 2024. "Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels," Energies, MDPI, vol. 17(6), pages 1-22, March.

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