Machine learning and agent-based modeling are two popular tools in energy research. In this article, we propose an innovative methodology that combines these methods. For this purpose, we develop an electricity price forecasting technique using artificial neural networks and integrate the novel...

Energy systems based on renewable energy sources require increasing demand side flexibility. Also, changes in the underlying cost structure, i. e., decreasing variable costs and increasing infrastructure investments, and varying customer needs should be reflected in the setup of future markets,...

Renewables introduce new weather-induced patterns and risks for market participants active in the energy commodity sector. We present a flexible framework for power spot prices that is capable of incorporating a weather model for the joint distribution of local weather conditions. This not only...

A paradigm shift has to be realized in future energy systems with high shares of renewable energy sources. The electrical demand has to react to the fluctuating electricity generation of renewable energy sources. To this end, flexible electrical loads like electric heating devices coupled with...