Global energy demand is set to increase by 4.6% in 2021 - led by emerging and developing economies - above 2019 levels. Demand for all fossil fuels will increase significantly in 2021, with both coal and gas above 2019 levels.
Thanks to our Heating Circuit Optimization algorithm, Lynus is able to reduce heating by 25%. This is accomplished by taking the current and future weather and temperature into account.
With Anomaly Detection it is possible to identify unexpected items or events in data sets, which differ from the norm. With this solution we can monitor and identify spikes in energy consumption.
With the latest breakthroughs in control engineering and machine learning, it is possible to control traditional controlled systems using historical data better than was previously possible. An example of a control loop that can be revolutionized by machine learning algorithms is the heating circuit in a building. It is possible to use historical data to train models that learn the building-specific temperature dynamics and thus accurately predict the indoor temperature.
The Lynus Energy Management system, aided by machine learning, makes it possible to monitor, control and optimize the performance of power generation or transmission.
Reduction in energy costs
Reduction in CO₂ Emissions
Return on Investment
More in-House Power Consumption