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Geospatial Forecasting of Electric Energy in Distribution Systems Using Segmentation and Machine Learning with Convolutional Methods

Author

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  • Héctor Chávez

    (Faculty of Electrical and Electronic Engineering, National University of Engineering, Lima 15333, Peru)

  • Yuri Molina

    (Department of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil)

Abstract

This paper proposes an innovative methodology for geospatial forecasting of electrical demand across various consumption segments and scales, integrating machine learning and discrete convolution within the framework of global system projections. The study was conducted in two phases: first, machine learning techniques were utilized to classify and determine the relative growth of segments with similar consumption patterns. In the second phase, convolution methods were employed to produce accurate spatial forecasts by incorporating the influence of neighboring areas through a “core matrix” and accounting for geographical constraints in regions with and without consumption. The proposed approach enhances the precision of spatial forecasts, making it suitable for large-scale distribution systems and implementable within short timeframes. The proposed method was validated using data from a Peruvian distribution system serving over one million users, employing 204 historical records and analyzing three georeferenced consumption segments at scales of 1:10,000, 1:1000, and 1:100. The results demonstrate its effectiveness in forecasting across different time horizons, thereby contributing to improved planning of electrical infrastructure.

Suggested Citation

  • Héctor Chávez & Yuri Molina, 2025. "Geospatial Forecasting of Electric Energy in Distribution Systems Using Segmentation and Machine Learning with Convolutional Methods," Energies, MDPI, vol. 18(2), pages 1-28, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:424-:d:1570435
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    References listed on IDEAS

    as
    1. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    2. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    3. Qiangyi Li & Lan Yang & Shuang Huang & Yangqing Liu & Chenyang Guo, 2023. "The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China," Land, MDPI, vol. 12(8), pages 1-27, August.
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