Record link: 
http://hdl.handle.net/20.500.12708/228833
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Title: 
Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales
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Citation: 
Houben, N., Heleno, M., Li, H., Hong, T., Auer, H., Ajanovic, A., & Haas, R. (2026). Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales. Engineering Applications of Artificial Intelligence, 179(1), Article 115014. https://doi.org/10.1016/j.engappai.2026.115014
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Publisher DOI: 
10.1016/j.engappai.2026.115014
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Houben, Nikolaus 
Heleno, Miguel 
Li, Han 
Hong, Tianzhen 
Auer, Hans 
Ajanovic, Amela 
Haas, Reinhard 
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Organisational Unit: 
E370-03 - Forschungsbereich Energiewirtschaft und Energieeffizienz 
E056-09 - Fachbereich CO2 Refinery 
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Journal: 
Engineering Applications of Artificial Intelligence 
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ISSN: 
0952-1976
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Date (published): 
1-Sep-2026
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Number of Pages: 
22
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Publisher: 
PERGAMON-ELSEVIER SCIENCE LTD
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Peer reviewed: 
Yes
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Keywords: 
Short term electricity load forecasting
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Research Areas: 
Climate Neutral, Renewable and Conventional Energy Supply Systems: 100%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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