Forecasting of residential unit’s heat demands: a comparison of machine learning techniques in a real-world case study
Crossref DOI link: https://doi.org/10.1007/s12667-023-00579-y
Published Online: 2023-05-09
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Kemper, Neele http://orcid.org/0000-0003-2110-1006
Heider, Michael
Pietruschka, Dirk
Hähner, Jörg
Funding for this research was provided by:
Universität Augsburg
Text and Data Mining valid from 2023-05-09
Version of Record valid from 2023-05-09
Article History
Received: 22 October 2022
Accepted: 17 April 2023
First Online: 9 May 2023