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

Pablo Alejandro Mendez-Santos, Nathalia Alexandra Chacón-Reino, Luis Fernando Guerrero-Vásquez (), Jorge Osmani Ordoñez-Ordoñez and Paul Andrés Chasi-Pesantez
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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
Luis Fernando Guerrero-Vásquez: Telecommunications and Telematics Research Group, Universidad Politécnica Salesiana, Cuenca EC010103, Ecuador
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

Energies, 2025, vol. 18, issue 14, 1-33

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.

Keywords: multiple linear regression; power distribution costs; electricity price forecasting; emerging energy markets (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: 2025
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