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[Medžo Matúš, Sales Strategy Manager at SEAS] Slovenské
elektrárne is the biggest electricity provider in Slovakia.
We are making around two-thirds
of the electricity produced in country,
95% of which is without CO2 emission,
mainly thanks to nuclear power plants.
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Reliable weather forecasting is crucial using wind
and solar power plants to produce energy means
that any weather changes directly influence
electricity supply and pricing.
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[Ľubomir Franko, Sales Strategy Analyst at SEAS] Our analysts
used to download data onto desktops and work on it manually.
Furthermore, every prediction took nearly two hours,
and had to be repeated four times
a day which was very inefficient.
We looked for a way how to predict
weather more accurately and more quickly.
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[Michal Krásny, Applications Architect at SEAS]
In Slovenské elektrárne, we didn't have this kind of
enterprise integrated solutions which brings all these
things together — get data, fill the model with
them, and represent them in the target BI platform.
That's why we decided to use Microsoft Data
Platform service.
We started with Hackathon,
and we delivered proof of concept including
data ingestion, data transformation
and data aggregation in the target BI report
using Azure Data Services just in three days.
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[Maroš Bubán, Data Architect at SEAS]
The platform consists of several services working together.
Azure Data Factory copies
meteorological data from servers to data lake,
where it's applied to specific geographical area.
Our analysts overlap these
meteo information with historical data
of power generated from wind turbines
in order to train predictive models.
We are using Azure Databricks for model training.
At final Power BI generates a report predicting how
much energy will be produced for a specific area.
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[Ľubomir Franko, Sales Strategy Analyst at SEAS] With
the new solution we can now process data only in 15 minutes,
and furthermore, it all can be done
without any human intervention.
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[Medžo Matúš, Sales Strategy Manager at SEAS]
Our forecasts are faster and more reliable now,
and thanks to this our sales department can react
quicker to the changes in electricity trading situation.
We are saving around 100 000 euros a year,
and our work is also smoother, and employees
can use their time more efficiently.
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