Detecção e correção de outliers em curvas de demanda de energia utilizando redes neurais artificiais autoencoders

• Ano de defesa: 2023
• Autor(a) principal: Pimentel, Levi da Costa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Engenharia Elétrica; Programa de Pós-Graduação em Engenharia Elétrica; UFPB
• Palavras-chave em Português: Inteligência artificial; Medidores inteligentes; Redes Neurais Artificiais (RNA); Redes elétricas inteligentes; Detecção e correção de outliers; Autoencoders; Artificial intelligence; Smart meters; Artificial Neural Networks (ANN); Smart grids; Outlier detection and correction; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/27059

One of the main problems encountered in Smart Grids is the occurrence of outliers, which can corrupt data, thus modifying the information brought by them, making it difficult for electrical system operators to make decisions based on this information. Therefore, this work proposes an integrated outlier detection and correction methodology, based on artificial neural networks. More specifically, a detection system based on Autoencoders was developed, with the aid of a softmax layer, and a correction system based on Autoencoders. The proposed methodology was contemplated in several scenarios, using data from a real substation, where the influence of the variation in the number of outliers present in the database, as well as the variation of their amplitude, on the functioning of the algorithms, is evaluated. In the tests performed, the detection technique achieved Accuracy and F-scores greater than 99.7% and 97.4%, respectively. The correction technique obtained MAPE mean absolute percentage error of 1.42%, while the root mean square error remained, in most of the evaluated scenarios, below 0.15 MW, a value that represents about 1.7% of the maximum power value available in the database.