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Machine learning-based models for self-learning indoor heat warning systems in households

  • With climate change and global rising temperatures heat health warning systems have become important in accurately predicting heat waves. However, most heat health warning systems rely on the ambient temperature forecast and do not take indoor building conditions into consideration. Moreover, a general heat warning system cannot accurately predict the heat stress conditions in individualWith climate change and global rising temperatures heat health warning systems have become important in accurately predicting heat waves. However, most heat health warning systems rely on the ambient temperature forecast and do not take indoor building conditions into consideration. Moreover, a general heat warning system cannot accurately predict the heat stress conditions in individual buildings. To implement the prediction algorithms the study also proposes a Raspberry Pi based measurement system. Furthermore, to reduce the computational load on Raspberry Pi a Transfer learning technique is implemented from a pre trained Long Short-Term Memory (LSTM) neural network. The results show prediction accuracy of 97% with an RMSE of 0.218 for indoor temperature prediction.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/10145
Bibliografische Angaben
Title (English):Machine learning-based models for self-learning indoor heat warning systems in households
Conference:The Upper Rhine Artificial Intelligence Symposium (5. : 16-17 November 2023 : Mulhouse, France)
Author:Oscar Villegas MierStaff MemberORCiDGND, Willi HaagStaff MemberGND, Raghavakrishna DevineniStaff Member, Guilherme Carraro CarellaStaff Member, Rainer GasperStaff MemberGND, Jens PfafferottStaff MemberORCiDGND, Michael SchmidtStaff MemberORCiDGND
Year of Publication:2024
Creating Corporation:ENSISA-IRIMAS
First Page:21
Last Page:30
Parent Title (English):UR-AI2023 : The Upper-Rhine Artificial Intelligence Symposium : Artificial Intelligence for Time Series, Robotics and Beyond
Editor:Jean-Philippe Lauffenburger, Jonathan Weber
URL:https://urai2023.sciencesconf.org/data/pages/book_urai2023_en_2024.pdf
Language:English
Inhaltliche Informationen
Institutes:Forschung / INES - Institut für nachhaltige Energiesysteme
Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Fakultät Maschinenbau und Verfahrenstechnik (M+V)
Collections of the Offenburg University:Bibliografie
Tag:Blac-box models; Grey-box models; Neural networks; Self learning; building thermal dynamycs; heat warning
Formale Angaben
Relevance for "Jahresbericht über Forschungsleistungen":Konferenzbeitrag: h5-Index < 30
Open Access: Open Access 
 Bronze 
Licence (German):License LogoUrheberrechtlich geschützt