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Advanced statistical learning on short term load process forecasting

Junjie Hu, Brenda López Cabrera and Awdesch Melzer

No 2021-020, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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 (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 Q31 Q41 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021020

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