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Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Author

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  • Iuri C. Figueiró

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
    Campus of Santo Ângelo, Integrated Regional University of Alto Uruguai and Missões, Santo Ângelo 98802-470, Rio Grande do Sul, Brazil)

  • Alzenira R. Abaide

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Nelson K. Neto

    (Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Rio Grande do Sul, Brazil)

  • Leonardo N. F. Silva

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Laura L. C. Santos

    (Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Rio Grande do Sul, Brazil)

Abstract

Activities related to the planning and operation of power systems use premise load forecasting, which is responsible for providing a load estimative for a given horizon that assists mainly in the operation of an electrical system. Hierarchical short-term load forecasting (STLF) becomes an approach used for this purpose, where the overall forecast is performed through system partition in smaller macro-regions and, soon after, is aggregated to compose a global forecast. In this context, this paper presents a bottom-up STLF approach for macro-regions. The main innovation is the Average Consumption per Meteorological Region (CERM) index, used to weigh the importance of each station meteorological (EM) in total load demand. Another index, the Variation of Load and Temperature (IVCT), based on historical temperature and demand changes, is proposed. These indexes are incorporated into an ANN model of the multi-layer perceptron type (MLP). The results showed a higher average performance of the index CERM and variable IVCT in relation to the other combinations performed, and the best results were used to compose the prediction of the MTR. Finally, the proposed model presented a Mean Absolute Percentage Error lower than 1%, presenting superior performance compared to an aggregate model for MTR, which shows the efficiency and contribution of the proposed methodology.

Suggested Citation

  • Iuri C. Figueiró & Alzenira R. Abaide & Nelson K. Neto & Leonardo N. F. Silva & Laura L. C. Santos, 2023. "Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region," Energies, MDPI, vol. 16(19), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6857-:d:1249699
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    References listed on IDEAS

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    1. Benjamin Auder & Jairo Cugliari & Yannig Goude & Jean-Michel Poggi, 2018. "Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting," Energies, MDPI, vol. 11(7), pages 1-22, July.
    2. Marcelo Bruno Capeletti & Bruno Knevitz Hammerschmitt & Renato Grethe Negri & Fernando Guilherme Kaehler Guarda & Lucio Rene Prade & Nelson Knak Neto & Alzenira da Rosa Abaide, 2022. "Identification of Nontechnical Losses in Distribution Systems Adding Exogenous Data and Artificial Intelligence," Energies, MDPI, vol. 15(23), pages 1-23, November.
    3. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac & Valentin A. Boicea, 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study," Energies, MDPI, vol. 14(13), pages 1-19, July.
    4. Pekka Koponen & Jussi Ikäheimo & Juha Koskela & Christina Brester & Harri Niska, 2020. "Assessing and Comparing Short Term Load Forecasting Performance," Energies, MDPI, vol. 13(8), pages 1-17, April.
    5. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
    6. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
    7. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    8. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
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