  Short Term Load Forecast (STLF) is necessary for effective scheduling,
operation optimization trading, and decision-making for electricity consumers.
Modern and efficient machine learning methods are recalled nowadays to manage
complicated structural big datasets, which are characterized by having a
nonlinear temporal dependence structure. We propose different statistical
nonlinear models to manage these challenges of hard type datasets and forecast
15-min frequency electricity load up to 2-days ahead. We show that the
Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) models applied
to the production line of a chemical production facility outperform several
other predictive models in terms of out-of-sample forecasting accuracy by the
Diebold-Mariano (DM) test with several metrics. The predictive information is
fundamental for the risk and production management of electricity consumers.
