Spatio-Temporal Missing Data Imputation for Smart Power Grids

Abstract

Availability of high fidelity timeseries data is imperative for critical power grid operational tasks such as state estimation, DER scheduling, etc. However, the data obtained from the metering infrastructure is prone to disruptions due to communication outages leading to missing values. State-of-the-art smart power grid Missing Data Imputation (MDI) algorithms either operate on individual timeseries and are unable to capture spatial dependencies due to the power grid topology or they operate on the entire dataset, requiring complex models which lead to overfitting.