Most renewable energy sources (RES; e.g., windmills or solar panels) pose the challenge that the production depends on external factors such as wind speed and the amount of sunlight. Hence, available power from RES cannot be planned as traditional energy sources. As a result, there is the need for balancing energy demand and supply in order to integrate more renewable energy sources. Accurate and efficient forecasts for short-term and mid-term horizons of energy consumption and production are a fundamental precondition for this dynamic and fine-grained scheduling of energy demand and supply. The state-of-the-art of forecasting energy demand and supply mainly focus on high accuracy forecasting of energy demand, while only few techniques exist for energy supply. In addition, there are further challenges. First, with regard to balancing energy demand and supply, forecasting takes place in a distributed system architecture that is inherently given by the hierarchy of involved organizations. Second, the large scale of the distributed system, in combination with a continuous stream of updates, leads to the requirement of efficient forecasting and forecast model adaptation.