• Medientyp: E-Book
  • Titel: Advanced statistical learning on short term load process forecasting
  • Beteiligte: Hu, Junjie [Verfasser:in]; López Cabrera, Brenda [Verfasser:in]; Melzer, Awdesch [Verfasser:in]
  • Erschienen: Berlin: International Research Training Group 1792, [2021]
  • Erschienen in: IRTG 1792 discussion paper ; 2021,20
  • Umfang: 1 Online-Ressource (circa 26 Seiten); Illustrationen
  • Sprache: Englisch
  • Identifikator:
  • Schlagwörter: Short Term Load Forecast ; Deep Neural Network ; Hard Structure Load Process ; Graue Literatur
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: 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.
  • Zugangsstatus: Freier Zugang