Overview
The aim of this project is to implement an adaptive system for smoothing peak loads in a representative and real network environment using a holistic integrating approach. To this end, an intelligent energy management and consumption planning system will be tested and evaluated in a broad pilot and demonstration project using machine learning algorithms.
Based on real-time data from several thousand measuring points of the participating grid operators (levels 5-7), load peaks are to be smoothed by analyzing and controlling flexible consumers such as heat pumps, boilers, and e-charging stations by forecasting and coordinating energy production and consumption.
