How do I link energy data from different sources?

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Direct answer

You link energy data from different sources by first creating a fixed data structure for locations, meters, energy carriers and time periods. You then connect metering data, invoices, contracts, building data, charging station data and external sources via exports, APIs or data connections.

  • Set up a data model
  • Link APIs and exports
  • Monitor data quality
  • Combine costs and consumption
Linking energy data from different sources in an independent platform

Separate data sources versus an energy data layer

Energy insight only becomes truly powerful when several sources come together. Metering data explains consumption, invoices explain costs, contracts explain risks and operational data explains why patterns arise. Without a data structure, that quickly becomes unclear.

  • Start with a data model for locations, meters, sources and energy carriers.
  • Check time periods, units, missing values and duplicate data.
  • Combine metering data with costs, contracts and operational context.

Structure

Traditional approach

Each source has its own columns, time periods and definitions.

Modern approach

Sources are translated into a shared location and meter model.

Control

Traditional approach

Errors only surface in reports.

Modern approach

Data quality is monitored at import and analysis.

Insight

Traditional approach

Consumption, costs and contracts stay separate.

Modern approach

Data is combined into steering information.

Which sources do you often link?

Frequently used sources are smart meters, metering companies, supplier portals, invoices, EPEX prices, weather data, charging stations, building management systems and internal location data.

  • Metering data and quarter-hourly values.
  • Invoices and contracts.
  • Charging station and building data.
  • External price and weather data.

What makes linking difficult?

Sources differ in time zone, interval, meter ID, unit, history and data quality. A good link resolves that in the data layer, not only in the dashboard.

  • Normalise units and energy carriers.
  • Align time periods.
  • Recognise duplicate or missing data.
  • Keep sources traceable.

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