Watt Analytics GmbH

Power consumption optimization with Artificial Intelligence & Machine Learning.

Power consumption optimization use cases and economic impact

Use cases and economic impact

Application areas of Watt Analytics technology

Our machine learning approach guarantees our customers continuous improvement in various areas:

  • Machine learning with every additional consumer added,

  • in any organization

  • cross-industry learning

  • device / product catalog

Our process ensures the ongoing use of an increasingly broad database for improvements in the specific application:

  • Automated clustering

  • Supervised and unsupervised learning processes

power consumption optimization and areas of application

Possible areas of application

The Watt Analytics System is suitable:

  • For "small consumers" (<25,000 KWh) for monitoring and controlling all electrical devices (such as washing machines, boilers, etc.) via just one app.

  • For medium-sized electricity consumers (25,000 - 125,000 KWh) such as single-family houses, offices, small restaurants with "larger energy consumers" (such as electric car charging stations, electric heating, boilers, heat pumps, pool heating, air conditioning, etc.). This also includes medium-sized electricity consumers, taking any photovoltaic systems or house batteries into account. In addition to an increase in convenience, the optimization leads to an economic ROI within 1-3 years for the customer as well as for the energy supplier.

  • In the large consumer segment from 150,000 KWh and in industrial applications, depending on the implementation of previous energy efficiency measures, significant consumption optimizations can be achieved which in any case justify the economic effort of a PoC project. In addition, electricity consumption can be optimized by up to 40% to increase self-consumption or the load profile can be improved, thus reducing energy costs by 15% -25%.

  • For the users of industrial production machines, massive cost advantages can be anticipated in predictive maintenance.