Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case

Autor: Waldo Fajardo, Juan Gómez-Romero, Mónica Mite-León, Julio Barzola-Monteses, Mayken Espinoza-Andaluz
Jazyk: angličtina
Rok vydání: 2019
Předmět:
020209 energy
Geography
Planning and Development
lcsh:TJ807-830
lcsh:Renewable energy sources
Functional time series analysis
02 engineering and technology
Management
Monitoring
Policy and Law
ARIMA
production prediction
Energy policy
Production prediction
020401 chemical engineering
Hydroelectricity
0202 electrical engineering
electronic engineering
information engineering
Production (economics)
Autoregressive integrated moving average
0204 chemical engineering
Time series
lcsh:Environmental sciences
lcsh:GE1-350
hydroelectric power
Renewable Energy
Sustainability and the Environment
business.industry
lcsh:Environmental effects of industries and plants
Variable (computer science)
Electricity generation
lcsh:TD194-195
time series analysis
Hydroelectric power plants
Environmental science
Electricity
Water resource management
business
ARIMAX
Zdroj: Sustainability
Volume 11
Issue 23
Digibug. Repositorio Institucional de la Universidad de Granada
instname
Sustainability, Vol 11, Iss 23, p 6539 (2019)
Popis: 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.
This 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.
Databáze: OpenAIRE
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