2017-02-142017-02-142015-09-04MELO, Diemisom Carlos Romano de. Inteligência computacional aplicada à detecção e correção de outliers em séries temporais: estudo de caso em consumo de energia elétrica. Orientadora: Adriana Rosa Garcez Castro. 2015. 59 f. Dissertação (Mestrado em Engenharia Elétrica.) - Instituto de Tecnologia, Universidade Federal do Pará, Belém, 2015. Disponível em: http://repositorio.ufpa.br/jspui/handle/2011/7675. Acesso em:.https://repositorio.ufpa.br/handle/2011/7675The electric load prediction is a task that requires accurate models, as should properly influence the decision making in hydroelectric plants and power stations. These computer models are implemented from a data set that must faithfully represent the behavior of the variables. However, these data sets are quite common the presence of outliers, which arise due to sensor reading errors, errors in the actual processing system / storage of data or faults in the distribution system or power station. This paper proposes a new methodology based on Computational Intelligence for detection and treatment of outliers in time series of electric power load. An auto associative artificial neural network is used for outlier detection. Subsequently, it is reused together with a genetic algorithm to correct detected outliers. This approach was applied to a time series of electrical power load in the State of Pará. The computational experiments were performed using the MATLAB tool and the results demonstrate the efficiency of the proposal, which identified and corrected all virtual outliers introduced during the evaluation phase of the methodology.Acesso AbertoRedes neurais auto-associativasAlgoritmos genéticosAnálise de séries temporaisDetecção de outliersCorreção de outliersOutliers detectionOutliers correctionAuto-associative neural networksGenetic algorithmTime series analysisDissertaçãoCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA