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Short-Term Load Forecasting for Spanish Insular Electric Systems

Author

Listed:
  • Eduardo Caro

    (Statistics Laboratory, ETSII, University Politécnica de Madrid, C/José Gutiérrez Abascal, 2, 28006 Madrid, Spain)

  • Jesús Juan

    (Statistics Laboratory, ETSII, University Politécnica de Madrid, C/José Gutiérrez Abascal, 2, 28006 Madrid, Spain)

Abstract

In any electric power system, the Transmission System Operator (TSO) requires the use of short-term load forecasting algorithms. These predictions are essential for appropriate planning of the energy resources and optimal coordination for the generation agents. This study focuses on the development of a prediction model to be applied to the ten main Spanish islands: seven insular systems in the Canary Islands, and three systems in the Balearic Islands. An exhaustive analysis is presented concerning both the estimation results and the forecasting accuracy, benchmarked against an alternative prediction software and a set of modified models. The developed models are currently being used by the Spanish TSO (Red Eléctrica de España, REE) to make hourly one-day-ahead forecasts of the electricity demand of insular systems.

Suggested Citation

  • Eduardo Caro & Jesús Juan, 2020. "Short-Term Load Forecasting for Spanish Insular Electric Systems," Energies, MDPI, vol. 13(14), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3645-:d:384861
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    References listed on IDEAS

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    1. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    2. Cancelo, José Ramón, 1991. "Forecasting daily demand for electricity with multiple-input nonlinear transfer function models: a case study," UC3M Working papers. Economics 2808, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Miguel López & Carlos Sans & Sergio Valero & Carolina Senabre, 2019. "Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study," Energies, MDPI, vol. 12(7), pages 1-31, April.
    5. Manh-Hai Pham & T-A-Tho Vu & Duc-Quang Nguyen & Viet-Hung Dang & Ngoc-Trung Nguyen & Thu-Huyen Dang & The Vinh Nguyen, 2019. "Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam," Energies, MDPI, vol. 12(12), pages 1-19, June.
    6. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
    7. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    8. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    9. Rae-Jun Park & Kyung-Bin Song & Bo-Sung Kwon, 2020. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
    10. Juncheng Zhu & Zhile Yang & Monjur Mourshed & Yuanjun Guo & Yimin Zhou & Yan Chang & Yanjie Wei & Shengzhong Feng, 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches," Energies, MDPI, vol. 12(14), pages 1-19, July.
    11. Waqas Ahmad & Nasir Ayub & Tariq Ali & Muhammad Irfan & Muhammad Awais & Muhammad Shiraz & Adam Glowacz, 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine," Energies, MDPI, vol. 13(11), pages 1-17, June.
    12. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    13. Masoud Sobhani & Allison Campbell & Saurabh Sangamwar & Changlin Li & Tao Hong, 2019. "Combining Weather Stations for Electric Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-11, April.
    14. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    15. Wang, Yaoping & Bielicki, Jeffrey M., 2018. "Acclimation and the response of hourly electricity loads to meteorological variables," Energy, Elsevier, vol. 142(C), pages 473-485.
    16. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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    Cited by:

    1. Mohamed Massaoudi & Shady S. Refaat & Haitham Abu-Rub & Ines Chihi & Fakhreddine S. Oueslati, 2020. "PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-29, October.
    2. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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