  We propose a simple empirical scaling law that describes load forecasting
accuracy at different levels of aggregation. The model is justified based on a
simple decomposition of individual consumption patterns. We show that for
different forecasting methods and horizons, aggregating more customers improves
the relative forecasting performance up to specific point. Beyond this point,
no more improvement in relative performance can be obtained.
