Smart energy management with IoT software

Do you want to reduce your energy consumption and costs while increasing energy efficiency in your business? Traditional energy management is not enough here. You will only achieve a breakthrough when you link IoT data of your machines & plants with energy data and additionally correlate it with electricity price & weather forecasts. Find out how this is done and what role optimizer algorithms play in the process in our Solution Paper.

In our Solution Paper:

we show you how to save up to 30% on energy costs with the innovative connection of IoT and energy data
we provide insight into the pilot project of a top German automotive group, which is implementing an intelligent energy supply and control system for its plant with the help of our Smart Energy Solutions.
we explain how to implement your own first pilot for a cloud-based energy optimization system within 2 weeks

Benefits

Your advantages with Smart Energy Solutions

Reduce energy costs

Increase energy efficiency

Optimize energy storage systems

All data at a glance

Smart algorithms

Use case

Automotive

Challenge

A leading German car manufacturer has set itself the goal of producing its vehicles with 40% less energy consumption by 2030. A model site is used to pilot how smart energy solutions can be used to achieve intelligent energy supply and control.

Solution

Device Insight’s cloud-based energy optimization systems enable IoT-based collection and analysis of energy and machine data, combined with weather and electricity price forecasts. The result: predictions and recommendations for smart energy management, clearly presented in the dashboard. Alternatively, the forecasts can also be transmitted directly to the machines and plants. By doing this, both better planning and more efficient rapid countermeasures for flexible, consumption- and CO2-relevant parameters can be achieved- in the spirit of Smart Energy.

"The Ready UI framework of our Smart Energy Solutions is based on Device Insight's own IoT building blocks combined with services from hyperscalers. For an initial pilot, implementation takes an average of 2 weeks, and for an extended proof of value 4 to 8 weeks."

Thomas Stammeier

CTO, Device Insight