CNN-D3

Convolutional Neural Networks for Automated Systems of Thermal Comfort Control
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Updated 27 Jun 2024

CNN-D3

Convolutional Neural Networks for Automated Systems of Thermal Comfort Control

D3_72dpi

To use this model, please cite the article

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

Cite As

Serdar Erisen (2024). CNN-D3 (https://github.com/serdarch/CNN-D3), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2023a
Compatible with R2017a and later releases
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.0.1

Citation updates

1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.