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

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

The University of Girona and SENESCYT-Ecuador awarded the author with a pre-doctoral grant of Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación, (SENESCYT)— Ecuador. This work has been partially funded by the grant PID2020-117171RA-I00 funded by MCIN/AEI/10.13039/501100011033, the Government of Catalonia under 2017SGR1551 and the E-LAND project which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824388