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

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  • Julio Barzola-Monteses

    (Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, 090514 Guayaquil, Ecuador
    Department of Computer Science and Artificial Intelligence, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, 18071 Granada, Spain)

  • Mónica Mite-León

    (Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, 090514 Guayaquil, Ecuador)

  • Mayken Espinoza-Andaluz

    (Centro de Energías Renovables y Alternativas, Facultad de Ingeniería Mecánica y Ciencias de la Producción, Escuela Superior Politécnica del Litoral, ESPOL, 09-01-5863 Guayaquil, Ecuador)

  • Juan Gómez-Romero

    (Department of Computer Science and Artificial Intelligence, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, 18071 Granada, Spain)

  • Waldo Fajardo

    (Department of Computer Science and Artificial Intelligence, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, 18071 Granada, Spain)

Abstract

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.

Suggested Citation

  • Julio Barzola-Monteses & Mónica Mite-León & Mayken Espinoza-Andaluz & Juan Gómez-Romero & Waldo Fajardo, 2019. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case," Sustainability, MDPI, vol. 11(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6539-:d:288855
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    References listed on IDEAS

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    1. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
    2. Pfaff, Bernhard, 2008. "VAR, SVAR and SVEC Models: Implementation Within R Package vars," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i04).
    3. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    4. Cevallos-Sierra, Jaime & Ramos-Martin, Jesús, 2018. "Spatial assessment of the potential of renewable energy: The case of Ecuador," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1154-1165.
    5. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    6. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Matti Koivisto & Kaushik Das & Feng Guo & Poul Sørensen & Edgar Nuño & Nicolaos Cutululis & Petr Maule, 2019. "Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    9. Franses, Philip Hans, 2016. "A note on the Mean Absolute Scaled Error," International Journal of Forecasting, Elsevier, vol. 32(1), pages 20-22.
    10. Lee, Yerim & Hur, Jin, 2019. "A simultaneous approach implementing wind-powered electric vehicle charging stations for charging demand dispersion," Renewable Energy, Elsevier, vol. 144(C), pages 172-179.
    11. Sher, Farooq & Pans, Miguel A. & Afilaka, Daniel T. & Sun, Chenggong & Liu, Hao, 2017. "Experimental investigation of woody and non-woody biomass combustion in a bubbling fluidised bed combustor focusing on gaseous emissions and temperature profiles," Energy, Elsevier, vol. 141(C), pages 2069-2080.
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    1. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
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    3. Jose Erazo & Guillermo Barragan & Modesto Pérez-Sánchez & Clotario Tapia & Marco Calahorrano & Victor Hidalgo, 2022. "Geometrical Optimization of Pelton Turbine Buckets for Enhancing Overall Efficiency by Using a Parametric Model—A Case Study: Hydroelectric Power Plant “Illuchi N2” from Ecuador," Energies, MDPI, vol. 15(23), pages 1-18, November.

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