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The integration of renewable energy sources in single family homes is challenging. Advance knowledge of the demand of electrical energy, heat, and domestic hot water (DHW) is useful to schedule projectable devices like heat pumps. In this work, we consider demand time series for heat and DHW from 2018 for a single family home in Germany. We compare different forecasting methods to predict such demands for the next day. While the 1-day-back forecast method led to the prediction of heat demand, the N-day-average performed best for DHW demand when Unbiased Exponentially Moving Average (UEMA) is used with a memory of 2.5 days. This is surprising as these forecasting methods are very simple and do not leverage additional information sources such as weather forecasts.
The increasing share of renewable energy with volatile production results in higher variability of prices for electrical energy. Optimized operating schedules, e.g., for industrial units, can yield a considerable reduction of energy costs by shifting processes with high power consumption to times with low energy prices. We present a distributed control architecture for virtual power plants (VPPs) where VPP participants benefit from flexible adaptation of schedules to price forecasts while maintaining control of their operating schedule. An aggregator trades at the energy market on behalf of the participants and benefits from more detailed and reliable load profiles within the VPP.