IRTG1792DP2021 020
Advanced Statistical Learning on Short Term Load Process Forecasting
Junjie Hu
Brenda López Cabrera
Awdesch Melzer
Abstract:
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.
Keywords:
Short Term Load Forecast, Deep Neural Network, Hard Structure Load Process
JEL Classification:
C51, C52, C53, Q31, Q41