Intelligent Models to Identification and Treatment of Outliers in Electrical Load Data

The Electrical Load data are stored in each time interval generating big databases with high dimensional data. Each data stored contains significant information that can assist the planning and operation of electrical system. In the data analysis step, many aspects must be considered such as the data consistency and the identification and treatment of outliers. This is a critical step because data quality is directly reflected in the results of the planning and operation of electrical system. This paper proposes two models for the identification and treatment of outliers in electrical load data. The first model was built using the ensemble technique through a combination of individual models. The second model was created from an expert system that uses a rules database to detect outliers. The processing of the outliers detected is conducted through a combination of non-outliers load in the same time interval. To evaluate the performance, the models were applied in a historical load database measured in the Northeast of Brazil during year of 2006. The results showed that the proposed models showed satisfactory results in terms of detection as well in the treatment of outliers.