Pfeil hoch

We create added values from smart meter data

from energy transparency to energy intelligence

As part of the upcoming smart meter rollout, the digital infrastructure in Germany and Europe will be greatly expanded. This generates enormous amounts of data: Instead of the one meter reading per year previously supplied by analogue meters, more than 35,000 quarter-hourly values per metering point and year are available after the installation of smart meters. This treasure of data unfolds great opportunities for utilities and end users. With the help of these data sets, AI and data science technologies can generate added value and services that were previously unthinkable.

Icon - Home Office

From pattern recognition to process optimization

By using intelligent and self-learning algorithms, patterns and previously unknown correlations can be identified. This will provide more accurate forecasts and automations that facilitate the user's work and optimize processes.

Icon - Home Office

Achieving our goals with expertise

At GreenPocket, a team of data science experts is already developing new technologies, taking into account the various use cases. We have years of practical experience from projects such as the NILM project funded by the German Federal Ministry of Economics and Energy as well as from direct cooperation with the Fraunhofer IIS/EAS within the framework of the Start-up TechConnection.

Use cases

Both utilities and end consumers, especially from the business segment, can benefit from data science technologies:

Icon - Lastmanagement


By processing the large amount of user data, a more transparent and detailed depiction of the load distribution can be created. This allows load management to be controlled much more effectively, generating better offerings for, amongst others, smart city applications and municipal services.

In addition, behavior patterns can be derived from the data and thus a better understanding of customer needs can be developed. This means that, as a next consequence, usage-based offers and recommendations for your customers can be created and shared via push notification, without costly market research measures. The resulting improvement in service perception can, for example, lead to a noticeable reduction in the migration rate. This was shown by the PEAKapp project funded by Horizon 2020, in which the submission of customer-specific offers reduced the number of users willing to switch by as much as 50%.

Icon - Prognose

End consumers

Business customers can, for example, benefit from better forecasting tools that predict consumption based on factors such as order situation and weather conditions. Anomaly detection can also be improved by data science technologies: both the occurrence of trends and deviations from those trends can be automatically detected by intelligent algorithms. In the subsequent development step, predictive maintenance applications can predict damage to equipment before it can cause expensive costs.

GreenPocket is looking for partners!

The first data science features will be incorporated into our product lines by the middle of next year. If you are interested in a joint project for the development of data science technologies, please contact us at