Data Imputation Techniques Applied to the Smart Grids Environment

The electricity sector has added plenty of new technologies in recent years. Smart Grids are characterized by the use of monitoring and communication technologies almost in whole system. The application and use of such new technologies triggers a significant growth in the data number, increasing the amount of errors and missing data, thus hindering the analysis. In this context, this paper performs the modeling, implementation, validation and comparative analysis of four data imputation techniques: K-Nearest Neighbor, Median Imputation, Last Observation Carried Forward, and Makima. The aim is to verify if they could be applied to the electric segment - more specifically to the Smart Grids environment. The database used in the research is obtained from the electricity utility CEEE and its underground substations, located in southern Brazil. Following this, five simulation scenarios are created and one data set is removed, based on pre-established criteria. Finally, the techniques are applied and the new database is compared with the original one. From the simulation results, the technique which presented the best results is Makima, it is validated as robust to be applied in the Smart Grids environment, especially in electrical data missing from an electric power substation.

Automated summary

The electricity sector has adopted new technologies, especially in Smart Grids, characterized by the use of monitoring and communication technologies. However, the increase in data volume and errors hinders analysis. This study compares four data imputation techniques and validates the applicability of Makima in the Smart Grids environment. Simulation results show that Makima performs the best in handling missing electrical data from substations.