Production of electrical energy becomes more and more decentralized as part of energy transition. Existing energy infrastructure is not optimized to deal with decentralized energy production peaks, and as a result, slows down energy transition.
By using IoT and related analytics to measure, analyze, and match local consumption and production patterns in a smart energy grid, energy producers can balance energy peaks, as well as enable new forms of decentralized trading models for electrical energy.
We implemented a solution based on a set of smart home sensors and AI to study decentralized production and consumption patterns in private households. The solution allows produces to optimize energy consumption patterns as well as predict peaks to enable local trading of energy in a smart grid.
The solution was awarded by the German Government and won an innovation award.