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A Comparative Analysis of Machine Learning Algorithms for Aggregated Electric Chargepoint Load Forecasting

Li, Chang ORCID iD icon 1; Zhang, Miao ORCID iD icon 1; Förderer, Kevin ORCID iD icon 1; Matthes, Jörg 1; Hagenmeyer, Veit ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract:

With the development of electric vehicles in the last years, the number of electric chargepoints are expanding rapidly. Accordingly, the aggregated load demand from different electric chargepoints is increasing significantly. Due to the unpredictability of charging behaviour, it is difficult to build white-box models to analyse the patterns and to predict the load profiles, which is essential for other tasks such as demand side management. Thus, in this work, four different models based on machine learning and deep learning algorithms namely Random Forest (RF), Support Vector Regression (SVR), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) are applied to a massive real-world open dataset from the UK, published in 2018, to compare the forecast performance of each algorithm with the modified persistence model as the baseline. The raw data are first pre-processed to generate the aggregated load demand by hour and then used for training and forecasting with a predictive horizon of 72 hours. The results are compared by using two common descriptive statistics, i.e., normalized Root-Mean-Square Error (nRMSE) and Mean Absolute Percentage Error (MAPE). ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000172205
Veröffentlicht am 05.07.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 07.2024
Sprache Englisch
Identifikator ISSN: 2267-1242
KITopen-ID: 1000172205
HGF-Programm 37.12.02 (POF IV, LK 01) Design,Operation & Digitalization of the Future Energy Grids
Erschienen in 2024 9th International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2024), Marseille, May 9th-11th 2024, Ed.: J. Lin
Veranstaltung 9th International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2024), Marseille, Frankreich, 09.05.2024 – 11.05.2024
Verlag EDP Sciences
Seiten Art.-Nr.: 01004
Serie E3S Web of Conferences ; 545
Vorab online veröffentlicht am 04.07.2024
Nachgewiesen in Dimensions
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Globale Ziele für nachhaltige Entwicklung Ziel 7 – Bezahlbare und saubere Energie
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