A solution for geographically dispersed sites

Energy companies with different types of renewable energy sources scattered across the country feel inefficient. Reporting on production is late and also has gaps where information is sometimes missing. A lot of time and money is spent on collecting data from the different plants to get the overview they need.

Challenge

  • As the production facilities are of different types, from different ages and geographically dispersed, the collection of the information is a time-consuming task.
  • The result is formatted in different ways, not uniform and requires hand filtering.
  • Due to downtime in various customised retrieval solutions, data has also been lost and could not be recovered.
  • Ultimately, to access information of various kinds, you need to access different types of systems and contact different actors.

Solution

By implementing a central production information solution, a definitive hub for all information has finally been achieved. The different types of data sources are connected through existing and already developed interfaces, which are then mapped to a common final destination. There the data is formatted with uniform resolution, frequency, accuracy and units of measure. Configuring buffering avoids gaps in the information flow that arise in the event of connectivity failure.

Simple overview images set up give everyone access to the information and real-time reporting. The central solution now also stores all data historically in a structured and space-efficient way, providing an irreplaceable "data bank" for all production and future opportunities for Industry 4.0 with, for example, advanced analytics and Machine Learning.

Result

  • One solution - Only one tool to use to read information from all sites.
  • Homogeneous and stable collection - reduces rounds and costs to dispersed sites.
  • Real-time reporting - you can now act in time to apply more effective solutions to problems.
  • Data buffering - No data is lost but loaded when connectivity is restored.
  • Future-proof - Industry 4.0 compliant for Machine Learning and cloud computing.

In this article

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