ENERGY SERVICES

The energy market is a prominent place where data analytics can make a difference. For instance data analytics helps to make energy systems more flexible such that they can better accommodate supply from renewable energy sources. A sound data infrastructure and machine learning enable to monitor and predict solar park volumes in real time and to operate flexibility assets such as batteries and demand response technologies. We have years of experience in applying data analytics to deal with uncertainties that managers of power companies face. We work on specific projects for clients. But we have also developed services. Data analytics as a service for energy.

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Monitoring
SOLAR PARKS​

This solution enables the monitoring the status of multiple solar parks. It collects the data from many sources from different providers and provides you status information in real time. Companies responsible for the operations and maintenance of solar parks use this tool to identify quickly where service is needed. It monitors the performance ratio and the system shows you which parks underperform.

The service is flexible. It is easy to connect new solar parks even when it has components from different suppliers.

This service provides you with forecasts for production volumes of your solar park. We apply machine learning to predict volumes using historical production volumes and weather data. We communicate directly with your systems and deliver the forecasts directly to you or to system operators.

OPERATING AND OPTIMIZING
FLEXIBILITY ASSETS

Flexibility assets are for example demand response and power storage systems. We have developed data driven solutions that determine when it is optimal to charge and discharge batteries or to increase or decrease consumption. These flexibility assets are essential to accommodate renewable energy sources in power system. And our service helps you to optimize the flexibility within your power system.

We developed ways to identify what type of flexibility is needed in power markets by predicting the occurrence of extreme power prices. The methodology is novel and has been accepted for publication in an academic journal.

renewable energy data