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

Mario Flor, Sergio Herraiz and Ivan Contreras
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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

Energies, 2021, vol. 14, issue 20, 1-15

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.

Keywords: energy consumption clustering; spatial analysis; machine learning; recurrent neural network; smart meter; load profiles forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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