We create added values from smart meter data

Turning energy transparency into energy intelligence

The installation of smart meters / advanced metering infrastructure in residential, commercial and industrial buildings opens new possibilities for monitoring, supplying and optimizing energy. AMI has the ability to share data in virtually any granularity allowing the suppliers to understand loads needed, plan accordingly and offer their customers variable rates depending on time of day and overall consumption.
These enormous amounts of data open up great opportunities for energy suppliers and end users. AI and data science technologies can generate added value and services from it that were previously unthinkable.

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From pattern recognition to process optimization

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

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Achieving our goals through expertise

At GreenPocket, a team of data science experts is already working on the development of 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:

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Utilities

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

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%.

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End consumers

Corporate customers can, for example, benefit from superior 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 them 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 production downtime.