One of the main problems of the data acquired by power utilities is the presence of outliers affecting the database measurements in the electrical system. In this way, the distribution scenario analysis is damaged by false and/or absence data. This work proposes a new module to complement the Smart Meters. Two algorithms for outlier correction were developed using artificial intelligence techniques: fuzzy logic and neural networks. To demonstrate the developed methods applicability, a case of study is performed with real substation data. The results were relevant and demonstrated the two proposed algorithms feasibility.