Data Preprocessing Summary
One of the primary goals of OEDI SI is improved workflow that seamlessly connects data to algorithms. To this end, the data preprocessing tools transform inconsistent input dataset to consistent set of input and measurement data that can work with OEDI SI co-simulation platform. While the data preprocessing tools have a wider range of uses, we will limit this use case to two scenarios – Data Anonymization: AMI Data to Load Profile and Data Imputation. For the data anonymization scenario, we will consider the case where the user wants to utilize AMI data to create load profiles for any test system. This scenario is aimed at addressing situations where the user wants to use field measurement data with generic test system to test how a given application performs i.e., the field measurements were generated from a different system and then mapped to other test systems. In the second use case, we will mitigate issues related to partially missing streaming measurement data i.e., imputing data when there are missing data points in a stream of data. This is useful for applications such as distribution system state estimation.
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