  Transmission system operators have a growing need for more accurate
forecasting of electricity demand. Current electricity systems largely require
demand forecasting so that the electricity market establishes electricity
prices as well as the programming of production units. The companies that are
part of the electrical system use exclusive software to obtain predictions,
based on the use of time series and prediction tools, whether statistical or
artificial intelligence. However, the most common form of prediction is based
on hybrid models that use both technologies. In any case, it is software with a
complicated structure, with a large number of associated variables and that
requires a high computational load to make predictions. The predictions they
can offer are not much better than those that simple models can offer. In this
paper we present a MATLAB toolbox created for the prediction of electrical
demand. The toolbox implements multiple seasonal Holt-Winters exponential
smoothing models and neural network models. The models used include the use of
discrete interval mobile seasonalities (DIMS) to improve forecasting on special
days. Additionally, the results of its application in various electrical
systems in Europe are shown, where the results obtained can be seen. The use of
this library opens a new avenue of research for the use of models with discrete
and complex seasonalities in other fields of application.
