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.…
Document Type: | Conference Proceeding |
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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 Mier![]() ![]() ![]() ![]() |
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): | ![]() |