Intelligent Energy Management Based on Predictive Analytics
The key to effectively managing energy is knowing when energy will be needed and how much. Currently available energy management systems require human programming, someone to input a fixed schedule. Such a system does not account for variability of human behavior. ÜberEnergy, by contrast, understands that human behavior is dynamic and flexible, and has developed a data analytics system using inputs such as sensor data, GPS, iBeacons, and even weather reports to accurately predict through machine learning the unique home occupancy patterns of a customer and that home’s resulting energy needs.
Rolf Behrsing is an accomplished engineer, business executive, and entrepreneur from the Silicon Valley. After graduating from the University of California Berkeley, he worked at the Lawrence Berkeley National Laboratory as a Computer Scientist and later at Sun Microsystems. He then expanded into business development and accounts, with focus on global partnerships. While constructing his own home Rolf realized how much energy was wasted but could be saved if one looked beyond traditional systems and applied machine learning. In 2016 as the CEO and founder of the ÜberEnergy, Rolf moved with the team to Berlin.