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Electricity load forecasting for industrial microgrid and load management

  • As more energy systems from renewable sources and the electricity market becomes more volatile, new solutions to ensure the security of supply are tested. Microgrids offer a possibility to prevent construction downtime. In order to use the energy sources and storage facilities of the microgrid effectively, load forecasting algorithms are essential. Thus, in this study a short term load forecasting model for a construction company is designed, that will be used for managing a microgrid as well as the general load consumption. This paper proposes the use of the XGBoost algorithm for the 36-hour forecast including predictors based on past measurements as well as information extracted from the timestamp. Moreover, it is shown that including load profiles attained by conventional methods has the potential to improve the accuracy of the model.

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Metadaten
Author:Ariane Franke
URN:urn:nbn:de:bvb:898-opus4-68231
DOI:https://doi.org/10.25929/qbwt-zf40
Parent Title (English):Applied Research Conference 2022 Conference Proceedings: 4th July 2022, University of Applied Sciences Ansbach (Onsite Conference)
Publisher:Hochschule Ansbach
Place of publication:Ansbach
Editor:Christian Uhl, Michael Walter, Annika Stiehl
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2022
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2024/02/05
Tag:XGBoost; load forecast; load profile
First Page:405
Last Page:409
Konferenzangabe:ARC 2022
Andere Schriftenreihe:Konferenz im Forschungsmaster - Master of Applied Research, MAPRby
Institutes:Fakultät Elektro- und Informationstechnik
Fakultät Elektro- und Informationstechnik / Forschungsstelle für Energienetze und Energiespeicher (FENES) / Forschungsgruppe Energienetze
research focus:Energie und Mobilität
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG