  The increasing use of renewable energy sources with variable output, such as
solar photovoltaic and wind power generation, calls for Smart Grids that
effectively manage flexible loads and energy storage. The ability to forecast
consumption at different locations in distribution systems will be a key
capability of Smart Grids. The goal of this paper is to benchmark
state-of-the-art methods for forecasting electricity demand on the household
level across different granularities and time scales in an explorative way,
thereby revealing potential shortcomings and find promising directions for
future research in this area. We apply a number of forecasting methods
including ARIMA, neural networks, and exponential smoothening using several
strategies for training data selection, in particular day type and sliding
window based strategies. We consider forecasting horizons ranging between 15
minutes and 24 hours. Our evaluation is based on two data sets containing the
power usage of individual appliances at second time granularity collected over
the course of several months. The results indicate that forecasting accuracy
varies significantly depending on the choice of forecasting methods/strategy
and the parameter configuration. Measured by the Mean Absolute Percentage Error
(MAPE), the considered state-of-the-art forecasting methods rarely beat
corresponding persistence forecasts. Overall, we observed MAPEs in the range
between 5 and >100%. The average MAPE for the first data set was ~30%, while it
was ~85% for the other data set. These results show big room for improvement.
Based on the identified trends and experiences from our experiments, we
contribute a detailed discussion of promising future research.
