EnergismeControlling energy dataArtificial intelligence & energy consumption

Predicting your energy consumption to optimize your energy performance

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Beyond the challenge of collecting and storing massive, continuous and real-time heterogeneous data, a major challenge is the implementation of predictive models

To accurately predict energy consumption, a large amount of data from internal sources must be combined with influencing factors.

Energisme's platform has a predictive analysis module based on Artificial Intelligence and allows you to develop and implement a robust and efficient energy efficiency strategy.

What is Artificial Intelligence?

Artificial Intelligence is a constellation of technologies capable of giving machines the ability to perceive, understand, act and learn in order to augment human capabilities.

To be efficient, it must have access to “intelligent”, reliable and quality data. In the world of energy, Artificial Intelligence allows you to truly manage your energy efficiency based on fine-grained energy consumption analyses and precise predictions.

Artificial intelligence allows you to anticipate the occurrence of problems and ensure faster and more reliable decision-making.

How does the implementation of predictive models contribute to your energy efficiency?

Creating predictive models of your energy consumption involves:

Once these steps are completed, you can then cross-reference historical data with predictive data such as weather for example and develop predictive decision support models.

Collecting massive, heterogeneous, continuous, real-time data

Scaling to integrate growing volumes of data

Making this data reliable and intelligent, a prerequisite for reliable analysis

Detecting and correcting anomalies

Energisme develops artificial intelligence can accelerate your energy transition/performance

Energisme has developed the most complete solution on the market with a predictive analysis module. How does it work?

  • N’Gage, Energisme’s platform, uses available data to understand and model the operation of a site, an equipment or a machine

Thanks to the single or multi-factor modeling module, N’Gage can describe and explain consumption. The platform crosses all or parts of these data with other consumption variables such as operating times, production plans, weather data, … or any other factor of influence available.

  • N’Gage ensures the consistency of these models with the various standards in force

The results are then automatically verified and detailed with respect to these standards, allowing you to measure and compare your level of energy performance and to define improvement actions to be in compliance when necessary.


1. The International Energy Performance Measurement and Verification Protocol defines standards and terms and suggests best practices for energy and water efficiency projects.

2. The purpose of this international standard is to establish a common set of principles and guidelines for measuring and verifying (MetV) the energy performance and improving the energy performance of an organization.

Energisme compares your consumption patterns to achieve energy savings and build predictive models

To quantify the savings, Energisme compares the consumption patterns of the site in question before and after its action.

This type of analysis is also used in our predictive models. We use a model generated on past consumption to predict future consumption. 

Energisme can therefore calibrate a consumption model against the number of parts produced in a plant.

Based on the estimated production of parts in future years, Energisme can then simulate future consumption.


It is early 2017:

The client performed an industrial boiler change on 12/31/2016. Energisme calibrates its model with 2016 data (before action) and applies it to 2016 & 2017. On the year before the implementation of the actions, we can see the model (purple curve) that fits very well the real consumptions (histograms). On the year 2017 (after action), the purple curve shows, considering the conditions of the year 2017 (values of the different variables used in our model), what the consumption would be if I had continued to follow my 2016 model (without any action):

N'Gage gives a very visual representation of the differences in consumption

We can see on the graph that:

  • The model is much higher than the actual consumption
  • The difference between the two and therefore the savings realized thanks to our action
  • The delta represents the savings made. If we know the cost of the action –> ROI