Convolutional Neural Networks for Automated Systems of Thermal Comfort Control (version 2)
The model can also be developed from an earlier version CNN-D2 which is also uploaded to GitHub. https://github.com/serdarch/CNN-D3
To use or develop this model, please cite
Erişen, S. A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces. Buildings 2023, 13, 1824. https://doi.org/10.3390/buildings13071824
The related datasets are available on:
https://thingspeak.com/channels/1229234
They are also partially published in the selected articles below:
Erişen, S. IoT-Based Real-Time updating multi-layered learning system applied for a special care context during COVID-19. Cogent Eng. 2022, 9, 2044588. https://doi.org/10.1080/23311916.2022.2044588
Erişen, S. Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment. Sensors 2022, 22, 7001. https://doi.org/10.3390/s22187001