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Analysis of time series models for Brazilian electricity demand forecasting

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  • Velasquez, Carlos E.
  • Zocatelli, Matheus
  • Estanislau, Fidellis B.G.L.
  • Castro, Victor F.

Abstract

Electricity forecasting contributes to have an idea of the electricity needs for the expansion of the electric system, the availability of the power plants according to the installed capacity and the way of the electric system operation. Therefore, this work studied the three time series approximations and their combinations considering two analyses: the first one considers different historical data baseline for the Brazil (SIN) and its subsystems to forecast the electricity demand from 2021 to 2025, then the percent error with EPE predictions are calculated for the same period. The second approach analysis the percent error of the electricity demand from 2014 to 2019, with the real historical data for the Brazil (SIN) and its subsystems. The results indicates that the Regression with Seasonality has the best approaches, and the combination of the time series methods helps to reduce the error of the approximations. In addition, choice of the historical data has an important role to have a better approach by time series analysis.

Suggested Citation

  • Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003863
    DOI: 10.1016/j.energy.2022.123483
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    2. SujayKumar Reddy M & Gopakumar G, 2023. "PM-Gati Shakti: Advancing India's Energy Future through Demand Forecasting -- A Case Study," Papers 2308.07320, arXiv.org.
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    8. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    9. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
    10. Rubens A. Fernandes & Raimundo C. S. Gomes & Carlos T. Costa & Celso Carvalho & Neilson L. Vilaça & Lennon B. F. Nascimento & Fabricio R. Seppe & Israel G. Torné & Heitor L. N. da Silva, 2023. "A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits," Sustainability, MDPI, vol. 15(14), pages 1-37, July.
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    12. Luzia, Ruan & Rubio, Lihki & Velasquez, Carlos E., 2023. "Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average," Energy, Elsevier, vol. 274(C).

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