Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/246491 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2021-020
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Short Term Load Forecast
Deep Neural Network
Hard Structure Load Process
JEL: 
C51
C52
C53
Q31
Q41
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
3.55 MB





Publikationen in EconStor sind urheberrechtlich geschützt.