Arbeitspapier
Advanced statistical learning on short term load process forecasting
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.
- Language
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Englisch
- Bibliographic citation
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Series: IRTG 1792 Discussion Paper ; No. 2021-020
- Classification
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Wirtschaft
Model Construction and Estimation
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
Nonrenewable Resources and Conservation: Demand and Supply; Prices
Energy: Demand and Supply; Prices
- Subject
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Short Term Load Forecast
Deep Neural Network
Hard Structure Load Process
- Event
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Geistige Schöpfung
- (who)
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Hu, Junjie
López Cabrera, Brenda
Melzer, Awdesch
- Event
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Veröffentlichung
- (who)
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Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (where)
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Berlin
- (when)
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2021
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Hu, Junjie
- López Cabrera, Brenda
- Melzer, Awdesch
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Time of origin
- 2021
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