Comparison of Forecasting Methods for Energy Demands in Single Family Homes

Abstract:
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.