  This paper presents a study of models for forecasting the electrical load for
supermarket refrigeration. The data used for building the models consists of
load measurements, local climate measurements and weather forecasts. The load
measurements are from a supermarket located in a village in Denmark. Every hour
the hourly electrical load for refrigeration is forecasted for the following 42
hours. The forecast models are adaptive linear time series models. The model
has two regimes; one for opening hours and one for closing hours, this is
modelled by a regime switching model and two different methods for predicting
the regimes are tested. The dynamic relation between the weather and the load
is modelled by simple transfer functions and the non-linearities are described
using spline functions. The results are thoroughly evaluated and it is shown
that the spline functions are suitable for handling the non-linear relations
and that after applying an auto-regressive noise model the one-step ahead
residuals do not contain further significant information.
