Reconhecimento de Cargas em Sistemas Residenciais de Gestão de Energia por meio de Algoritmos Baseados em Árvores de Decisão

Thales Wulfert Cabral, Guto Mendes, Fernando Bauer, Eduardo de Lima, Gustavo Fraidenraich, Luís Geraldo P. Meloni

DOI: 10.14209/sbrt.2023.1570923175

Evento: XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2023)

Keywords: Machine learning Load Recognition XGBoost

Abstract

For the efficient management of domestic energy demand, technology has carried innovative equipment such as the Home Energy Management System (HEMS). As an extension of [5], this article adopts new alternatives for the fast training chain of models to load recognition in HEMS. The contributions are (i) the inclusion of Extreme Gradient Boosting (XGBoost) to load recognition, (ii) the addition of robust techniques for feature extraction, and (iii) new analyses employing these techniques along with the old tree-based models. Finally, the results indicate XGBoost as the winning alternative, achieving superior performance values compared to rivals.

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