To address the scaling challenges, Accenture utilized the Awesense Platform to ingest Evergy’s data and automate the quality assessment and improvement process.
After unifying the multiple data sources into Awesense’s Energy Data Model, the Awesense Data Engine was run to identify and suggest corrections for a multitude of issues in the dataset. This included missing or inaccurate feeder IDs (affecting over 45,000 devices), disconnected assets and islands (over 30,000), poor meter-to-transformer mapping (close to 20,000 orphaned meters) and phase mismatches between meters and transformers (over 15,000). The issues were ranked by impact priority and further investigation was performed by use of Awesense’s True Grid Intelligence.
Additionally, Awesense’s out-of-the-box energy load balancing capability was used to compare SCADA feeder-top load with aggregated downstream AMI consumption and identify discrepancies.
These findings equipped Evergy with a clear, data-driven roadmap for addressing gaps ahead of ADMS deployment.
The solution followed an iterative cycle — ingest → analyze → validate → refine — which enabled continuous improvement and deeper insight into grid data readiness. As said at the DTech MIDWEST conference, the pilot also demonstrated the scalability of the Awesense Platform as a foundational tool for continuous data quality monitoring, unlocking further use cases such as EV forecasting, energy balance and power factor analysis.
