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A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines

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  • Abdelmonaem Jornaz

    (Department of Mathematics and Statistics, Northwest Missouri State University, Maryville, MO 64468, USA)

  • V. A. Samaranayake

    (Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO 65409, USA)

Abstract

Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with different hours of the day and different days of the week having an effect on the load. This paper proposes a hybrid load-forecasting method that combines classical time series formulations with cubic splines to model electricity load. It is shown that this approach produces a model capable of making short-term forecasts with reasonable accuracy. In contrast to forecasting models that utilize a multitude of regressor variables observed at multiple time points within a day, only the hourly temperature is used in the proposed model and predictive power gains are achieved through the modeling of the 24-hour load profiles across weekends and weekdays while also taking into consideration seasonal variations of such profiles. Long-term trends are accounted for by using population and economic variables. The proposed approach can be used as a stand-alone predictive platform or be used as a scaffolding to build a more complex model involving additional inputs. The data cover the period from 1 January 1993 through 31 December 2013 from the Atlantic City Electric zone.

Suggested Citation

  • Abdelmonaem Jornaz & V. A. Samaranayake, 2019. "A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines," Energies, MDPI, vol. 12(21), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4169-:d:282440
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    References listed on IDEAS

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    1. Haeran Cho & Yannig Goude & Xavier Brossat & Qiwei Yao, 2013. "Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 7-21, March.
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    Cited by:

    1. Antonio Gabaldón & María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez, 2022. "Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives," Energies, MDPI, vol. 15(24), pages 1-5, December.
    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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