Machine learning-based models for self-learning indoor heat warning systems in households
Year: 2025
Type: article
Abstract: 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 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.
Authors: Villegas Mier, Oscar, Haag, Willi, Devineni, Raghavakrishna, Carraro Carella, Guillerme, Gasper, Rainer, Pfafferott, Jens, Schmidt, Michael
Institution: Offenburg University of Applied Sciences
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6901810665
FWCI:
Cites:
Cited by:
Related to: