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Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data

Semmelmann, Leo 1; Henni, Sarah 2; Weinhardt, Christof ORCID iD icon 1
1 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)
2 Institut für Programmstrukturen und Datenorganisation (IPD), Karlsruher Institut für Technologie (KIT)

Abstract:

Accurate day-ahead load forecasting is an important task in smart energy communities, as it enables improved energy management and operation of flexibilities. Smart meter data from individual households within the communities can be used to improve such forecasts. In this study, we introduce a novel hybrid bi-directional LSTM-XGBoost model for energy community load forecasting that separately forecasts the general load pattern and peak loads, which are later combined to a holistic forecasting model. The hybrid model outperforms traditional energy community load forecasting based on standard load profiles as well as LSTM-based forecasts. Furthermore, we show that the accuracy of energy community day-ahead forecasts can be significantly improved by using smart meter data as additional input features.


Verlagsausgabe §
DOI: 10.5445/IR/1000151263
Veröffentlicht am 21.10.2022
Originalveröffentlichung
DOI: 10.1186/s42162-022-00212-9
Scopus
Zitationen: 11
Dimensions
Zitationen: 13
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2520-8942
KITopen-ID: 1000151263
Erschienen in Energy Informatics
Verlag SpringerOpen
Band 5
Heft S1
Seiten Art.Nr. 24
Vorab online veröffentlicht am 07.09.2022
Nachgewiesen in Dimensions
Scopus
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