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Energy Consumption Forecasts by Gradient Boosting Regression Trees

Author

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  • Luca Di Persio

    (Department of Computer Science, University of Verona, 37134 Verona, Italy)

  • Nicola Fraccarolo

    (Department of Mathematics, University of Trento, 38123 Trento, Italy)

Abstract

Recent years have seen an increasing interest in developing robust, accurate and possibly fast forecasting methods for both energy production and consumption. Traditional approaches based on linear architectures are not able to fully model the relationships between variables, particularly when dealing with many features. We propose a Gradient-Boosting–Machine-based framework to forecast the demand of mixed customers of an energy dispatching company, aggregated according to their location within the seven Italian electricity market zones. The main challenge is to provide precise one-day-ahead predictions, despite the most recent data being two months old. This requires exogenous regressors, e.g., as historical features of part of the customers and air temperature, to be incorporated in the scheme and tailored to the specific case. Numerical simulations are conducted, resulting in a MAPE of 5–15% according to the market zone. The Gradient Boosting performs significantly better when compared to classical statistical models for time series, such as ARMA, unable to capture holidays.

Suggested Citation

  • Luca Di Persio & Nicola Fraccarolo, 2023. "Energy Consumption Forecasts by Gradient Boosting Regression Trees," Mathematics, MDPI, vol. 11(5), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1068-:d:1075171
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Luca Di Persio & Mohammed Alruqimi & Matteo Garbelli, 2024. "Stochastic Approaches to Energy Markets: From Stochastic Differential Equations to Mean Field Games and Neural Network Modeling," Energies, MDPI, vol. 17(23), pages 1-46, December.
    2. Meshari D. Alanazi & Ahmad Saeed & Muhammad Islam & Shabana Habib & Hammad I. Sherazi & Sheroz Khan & Mohammad Munawar Shees, 2023. "Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
    3. Luca Di Persio & Nicola Fraccarolo & Andrea Veronese, 2024. "Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks," Mathematics, MDPI, vol. 12(13), pages 1-16, July.
    4. Enes Gul & Efthymia Staiou & Mir Jafar Sadegh Safari & Babak Vaheddoost, 2023. "Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye," Sustainability, MDPI, vol. 15(15), pages 1-17, July.

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