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Why use Eneos

The power of Automation and Accuracy

Comparison between traditional approach and the automated solution of Eneos

Traditional approach

Without external variables

Not considering variability

No best/worst-case scenarios

No automatic action

Difficult to mitigate risk

"With Forecasting, you will be ready for the future."

Creation of 'Digital Twin'

First a model ('Digital Twin') has to be created. In this model are all significant variables included. These variables explain as much of the variation in the targeted value as possible. This will be the reference model. The targeted value can be anything with enough data available: electricity, water, gas, use of other resources,...

Application of the model

After creating the 'Digital Twin', it can be used to forecast the targeted value. If we expect an increase of activity, we can include this in the forecast. This makes it possible to use a distribution for each variable as an input. This leads to a minimum, average and maximum value at each time step, which can be used for risk assessments. The closer we make a forecast, the more accurate we can predict the targeted value.

(Energy) Purchasing Strategy

Forecasting is used in a wide range of fields. Forecasting is widely used for detailed predictions of the hourly energy use for the whole year. With this information it's possible to purchase the energy cheaper, and create a strategy on it.

There are two forecasts that have to be made:

  • Demand forecast: When and how much energy is the building going to consume, and what's the certainty of the forecast. Here it's important to consider changing variables, like increasing production rates or building occupation. The more accurate the energy demand is predicted, the better the result will be.
  • Energy price forecast: Make a forecast of the energy prices

Bringing these two forecasts together into an optimal strategy will minimize the energy cost.

phone number

+32 456.14.12.73

Email

info@eneos.cloud

Address

Rolwagenstraat 63
2018 Antwerpen