AI-Driven Data Cleansing & Structuring for Utilities
The Awesense AI Data Engine uses AI for data cleansing – doing all the heavy lifting for data cleansing & structuring, enabling modeling, analytics, and use case development in record time. In the AI Data Engine, data is ingested, cleansed & structured, then synchronized in time & space. The result of this computation is the Awesense Energy Data Model (EDM) grid digital twin which is further available easily via APIs.
Any & All Data Sources
The AI Data Engine can process multiple data sources, and new data types can be added incrementally.
Trust Your Data With The AI Data Engine
Validation & Correction
- Geo-coordinates validation & transformation
- Connectivity validation, estimation & correction
- Switch state validation & correction
- Meter association validation
Estimation & Synchronization
- Time series data validation & synchronization
- Missing time series data estimation
Discovery Of Blind Spots
- Exposes locations in the grid where consumers, particularly large industrials, are likely missing from the dataset
Data Model-Driven Digital Twin of the Grid
The data is ingested, cleansed, synchronized and structured according to the Awesense Energy Data Model (EDM), resulting in an EDM-driven digital twin of the grid. EDM is designed to be extensive while still easy to process and understand, and it balances flexibility with standardization.The EDM-driven digital twin is readily available through APIs so that models, use cases & analytics can be easily built. It can also be viewed through the Awesense Visual Data Explorer, True Grid Intelligence (TGI) or any industry-standard business intelligence tool.
Make Reliable Data-Proven Decisions
Dependable utility data is the foundation for all changes occurring with the energy transition. Validated, corrected, and synchronized data enables the decisions needed for regulatory approval and the coming wave of policy changes. This process is typically time-consuming and expensive with multiple parties involved. Awesense accelerates this process by structuring the data into a model-driven digital twin.
Awesense accelerates this process: