Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case
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AuthorBarzola Monteses, Julio; Mite León, Mónica; Espinoza Andaluz, Mayken; Gómez Romero, Juan; Fajardo Contreras, Waldo
Hydroelectric power plantsProduction predictionFunctional time series analysisARIMAARIMAX
Barzola-Monteses, J., Mite-León, M., Espinoza-Andaluz, M., Gómez-Romero, J., & Fajardo, W. (2019). Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case. Sustainability, 11(23), 6539.
SponsorshipThis work has been funded by the Universidad de Guayaquil through the grant number FCI-015-2019. This work has been also supported by ESPOL, grant number FIMCP-CERA-05-2017.
Electrical generation in Ecuador mainly comes from hydroelectric and thermo-fossil sources, with the former amounting to almost half of the national production. Even though hydroelectric power sources are highly stable, there is a threat of droughts and floods affecting Ecuadorian water reservoirs and producing electrical faults, as highlighted by the 2009 Ecuador electricity crisis. Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions. In this paper, we developed a time series predictive model of hydroelectric power production in Ecuador. To this aim, we used production and precipitation data from 2000 to 2015 and compared the Box-Jenkins (ARIMA) and the Box-Tiao (ARIMAX) regression methods. The results showed that the best model is the ARIMAX (1,1,1) (1,0,0)12, which considers an exogenous variable precipitation in the Napo River basin and can accurately predict monthly production values up to a year in advance. This model can provide valuable insights to Ecuadorian energy managers and policymakers.