EcoStruxure Grid Data Hub
بواسطة Schneider Electric
Transversal grid intelligence layer. Unlocking actionable insights through unified data across grid.
About the offer:
Grid Data Hub (GDH) is a centralized data analytics and integration platform used to efficiently collect, process, store, and distribute large volumes of grid-related data. It acts as a unified layer between data sources, such as smart meters, sensors, SCADA systems, and IoT devices—and downstream applications like billing, analytics, forecasting, and customer engagement tools.
At its core, Grid Data Hub consolidates disparate data streams into a single, standardized data model. This ensures consistency, data quality, and accessibility across an organization. By harmonizing data formats and applying validation rules, GDH helps eliminate data silos and reduces inconsistencies that traditionally arise when multiple systems operate independently. It serves as the foundation for AI and machine learning applications, predictive maintenance, and smart grid optimization strategies by providing clean, structured, and timely data.
Key benefits
- Native connectors for ingesting the data from other Schneider Electric applications such as EcoStruxure ADMS and EcoStruxure DERMS, with full flexibility for building custom ingestion pipelines for other systems.
- Broad, standards-based utility-specific data model with support for multiple domains (SCADA, OMS, DERMS, GIS, etc) and multiple data types (time-series, referential, spatial, unstructured, etc). GDH comes with meta-model that carries all the necessary information about the entities, their attributes and the relationship between entities, so that it can be used for training AI models.
- Embedded AI assistant which can be used for creating reports and insights with no-code or low-code approach, just by using natural language.
- Supports sensitivity labeling on the source side to ensure privacy of sensitive data is enforced wherever the data is being used.
- Scalable and flexible, with the capability to accommodate different volumes of the data, and all kinds of transformations.