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Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia

Author

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  • Julián Urrego-Ortiz

    (Grupo de Ingeniería y Gestión Ambiental (GIGA), Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia)

  • J. Alejandro Martínez

    (Grupo de Ingeniería y Gestión Ambiental (GIGA), Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia)

  • Paola A. Arias

    (Grupo de Ingeniería y Gestión Ambiental (GIGA), Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia)

  • Álvaro Jaramillo-Duque

    (Research Group in Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Calle 67 No. 53–108, Medellín 050010, Colombia)

Abstract

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m 2 . This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m 2 and ~170 W/m 2 (~50%–~110%) under overcast conditions, and ~57 W/m 2 to ~171 W/m 2 (~16%–~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.

Suggested Citation

  • Julián Urrego-Ortiz & J. Alejandro Martínez & Paola A. Arias & Álvaro Jaramillo-Duque, 2019. "Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia," Energies, MDPI, vol. 12(22), pages 1-29, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4402-:d:288686
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    References listed on IDEAS

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    1. Zempila, Melina-Maria & Giannaros, Theodore M. & Bais, Alkiviadis & Melas, Dimitris & Kazantzidis, Andreas, 2016. "Evaluation of WRF shortwave radiation parameterizations in predicting Global Horizontal Irradiance in Greece," Renewable Energy, Elsevier, vol. 86(C), pages 831-840.
    2. Munir Husein & Il-Yop Chung, 2019. "Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach," Energies, MDPI, vol. 12(10), pages 1-21, May.
    3. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    4. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    5. Doorga, Jay R.S. & Rughooputh, Soonil D.D.V. & Boojhawon, Ravindra, 2019. "Modelling the global solar radiation climate of Mauritius using regression techniques," Renewable Energy, Elsevier, vol. 131(C), pages 861-878.
    6. Lan, Hai & Yin, He & Hong, Ying-Yi & Wen, Shuli & Yu, David C. & Cheng, Peng, 2018. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route," Applied Energy, Elsevier, vol. 211(C), pages 15-27.
    7. Bouabdallah, A. & Olivier, J.C. & Bourguet, S. & Machmoum, M. & Schaeffer, E., 2015. "Safe sizing methodology applied to a standalone photovoltaic system," Renewable Energy, Elsevier, vol. 80(C), pages 266-274.
    8. Nguyen, B.T. & Pryor, T.L., 1996. "A computer model to estimate solar radiation in Vietnam," Renewable Energy, Elsevier, vol. 9(1), pages 1274-1278.
    9. Li, Shuai & Ma, Hongjie & Li, Weiyi, 2017. "Typical solar radiation year construction using k-means clustering and discrete-time Markov chain," Applied Energy, Elsevier, vol. 205(C), pages 720-731.
    10. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    1. Henao, Felipe & Viteri, Juan P. & Rodríguez, Yeny & Gómez, Juan & Dyner, Isaac, 2020. "Annual and interannual complementarities of renewable energy sources in Colombia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    2. Gil Ruiz, Samuel Andrés & Cañón Barriga, Julio Eduardo & Martínez, J. Alejandro, 2022. "Assessment and validation of wind power potential at convection-permitting resolution for the Caribbean region of Colombia," Energy, Elsevier, vol. 244(PB).

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