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Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis

Author

Listed:
  • Mario Flor

    (Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain)

  • Sergio Herraiz

    (Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain)

  • Ivan Contreras

    (Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain)

Abstract

This study presents a novel approach for discovering actionable knowledge and exploring data-based models from data recorded by household smart meters. The proposed framework is supported by a machine learning architecture based on the application of data mining methods and spatial analysis to extract temporal and spatial restricted clusters of characteristic monthly electricity load profiles. In addition, it uses these clusters to perform short-term load forecasting (1 week) using recurrent neural networks. The approach analyses a database with measurements of 1000 smart meters gathered during 4 years in Guayaquil, Ecuador. Results of the proposed methodology led us to obtain a precise and efficient stratification of typical consumption patterns and to extract neighbour information to improve the performance of residential energy consumption forecasting.

Suggested Citation

  • Mario Flor & Sergio Herraiz & Ivan Contreras, 2021. "Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis," Energies, MDPI, vol. 14(20), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6565-:d:654668
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    References listed on IDEAS

    as
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