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A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management
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Orientador(es)
Resumo(s)
The integration of the Smart Grid concept into the
electric grid brings to the need for an active participation of
small and medium players. This active participation can be
achieved using decentralized decisions, in which the end
consumer can manage loads regarding the Smart Grid needs.
The management of loads must handle the users’ preferences,
wills and needs. However, the users’ preferences, wills and needs
can suffer changes when faced with exceptional events. This
paper proposes the integration of exceptional events into the
SCADA House Intelligent Management (SHIM) system
developed by the authors, to handle machine learning issues in
the domestic consumption context. An illustrative application
and learning case study is provided in this paper.
Descrição
Palavras-chave
Domestic consumption Exceptional events Intelligent load management Machine learning Smart Grid
Contexto Educativo
Citação
Editora
IEEE