msf, a forecasting library to predict short-term electricity demand based on multiple seasonal time series

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. On many occasions, the resources to reach these predictions are not available to researchers. In this paper we present a MATLAB (R) toolbox created to predict electrical demand. The toolbox implements multiple seasonal time series forecasting methods such as new generalized multiple seasonal Holt-Winters exponential smoothing models and neural network models, among others. The models presented include novel discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. The paper also describes the computational analysis conducted to apply the toolbox in various electrical systems in Europe, where the results obtained can be seen. The use of this library opens a new way of research for the use of models with discrete and complex seasonalities in other fields of application.