mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters
<|reference_start|>mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters: 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.<|reference_end|>
@article{trull2024mshw,,
    title={mshw, a forecasting library to predict short-term electricity demand
  based on multiple seasonal Holt-Winters},
    author={Oscar Trull, and J. Carlos Garc'ia-D'iaz, and Angel Peir'o-Signes},
    journal={Journal of Computational Science Volume 78, June 2024, 102280},
    year={2024},
    doi={10.1016/j.jocs.2024.102280},
    archivePrefix={arXiv},
    eprint={2402.10982},
    primaryClass={cs.LG econ.EM stat.AP}
}