dc.contributor.advisor | Barbosa, Jorge Luis Victória | |
dc.contributor.author | Costa, Ricardo dos Santos | |
dc.date.accessioned | 2023-02-17T18:07:23Z | |
dc.date.accessioned | 2023-03-22T20:07:47Z | |
dc.date.available | 2023-02-17T18:07:23Z | |
dc.date.available | 2023-03-22T20:07:47Z | |
dc.date.issued | 2022-10-05 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12032/80155 | |
dc.description.abstract | The growing demand for electricity and requirements imposed by regulatory agencies
have led the conventional electricity distribution system to evolve into the context of
smart grids. Data acquisition and analysis are central issues for the evolution of the
distribution system. Machine learning technologies are gaining ground in applied
studies, making this an emerging topic. The related studies do not address the
preparation and analysis of data before application in artificial intelligence models. In
this context, this work presents a model capable of performing the pre-processing,
classification, and prediction of electrical quantities. Compared with related works,
there is an indication that this is the first study that addresses the pre-processing and
grouping of data by similarity to increase the effectiveness of artificial intelligence
models. The Fuzzy C-Means method for data classification allows outliers to be found
more assertively. The Grubbs method identifies critical operating points of the system.
The regression stage presents predictions made with LSTM neural networks up to fourtime steps ahead with a percentage absolute average error of 0.16% using a real
database of an electric power distribution utility. | en |
dc.description.sponsorship | Nenhuma | pt_BR |
dc.language | pt_BR | pt_BR |
dc.publisher | Universidade do Vale do Rio dos Sinos | pt_BR |
dc.rights | openAccess | pt_BR |
dc.subject | Pré-processamento de dados | pt_BR |
dc.subject | Data pre-processing | en |
dc.title | PCR: um modelo híbrido para previsão de grandezas elétricas aplicado em estudo de caso de um regulador de tensão em operação | pt_BR |
dc.type | Dissertação | pt_BR |