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Estimation and Forecasting of the Average Unit Cost of Energy Supply in a Distribution System Using Multiple Linear Regression and ARIMAX Modeling in Ecuador

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Listed:
  • Pablo Alejandro Mendez-Santos

    (Distribution Department, Empresa Eléctrica Regional Centro Sur C.A., Ave Max Uhle y Av. Pumapungo, Cuenca EC010150, Ecuador)

  • Nathalia Alexandra Chacón-Reino

    (Telecommunications and Telematics Research Group, Universidad Politécnica Salesiana, Cuenca EC010103, Ecuador
    These authors contributed equally to this work.)

  • Luis Fernando Guerrero-Vásquez

    (Telecommunications and Telematics Research Group, Universidad Politécnica Salesiana, Cuenca EC010103, Ecuador
    These authors contributed equally to this work.)

  • Jorge Osmani Ordoñez-Ordoñez

    (Telecommunications and Telematics Research Group, Universidad Politécnica Salesiana, Cuenca EC010103, Ecuador)

  • Paul Andrés Chasi-Pesantez

    (Telecommunications and Telematics Research Group, Universidad Politécnica Salesiana, Cuenca EC010103, Ecuador)

Abstract

The accurate estimation of electricity supply costs has become increasingly relevant due to growing demand, variable generation sources, and regulatory changes in emerging power systems. This study models the average unit cost of electricity supply (USD/kWh) in Ecuador using multiple linear regression techniques and ARIMAX forecasting, based on monthly data from 2018 to 2024. The regression models incorporate variables such as energy demand, generation mix, transmission costs, and regulatory indices. To enhance model robustness, we apply three variable selection strategies: correlation analysis, PCA, and expert-driven selection. Results show that all models explain over 70% of price variability, with the highest-performing regression model achieving R 2 = 0.9887 . ARIMAX models were subsequently implemented using regression-based forecasts as exogenous inputs. The ARIMAX model based on highly correlated variables achieved a MAPE below 5%, showing high predictive accuracy. These findings support the use of hybrid statistical models for informed policy-making, tariff planning, and operational cost forecasting in structurally constrained energy markets.

Suggested Citation

  • Pablo Alejandro Mendez-Santos & Nathalia Alexandra Chacón-Reino & Luis Fernando Guerrero-Vásquez & Jorge Osmani Ordoñez-Ordoñez & Paul Andrés Chasi-Pesantez, 2025. "Estimation and Forecasting of the Average Unit Cost of Energy Supply in a Distribution System Using Multiple Linear Regression and ARIMAX Modeling in Ecuador," Energies, MDPI, vol. 18(14), pages 1-33, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3659-:d:1699201
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    References listed on IDEAS

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    1. Ochoa, Camila & van Ackere, Ann, 2015. "Does size matter? Simulating electricity market coupling between Colombia and Ecuador," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1108-1124.
    2. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    3. Arkadiusz Jędrzejewski & Grzegorz Marcjasz & Rafał Weron, 2021. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO," Energies, MDPI, vol. 14(11), pages 1-17, June.
    4. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    5. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    6. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
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