ANN prediction model to improve employees’ thermal satisfaction in tropical green office buildings
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00779-y.pdf
Reference51 articles.
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2. Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4, 938. https://doi.org/10.1016/j.heliyon.2018
3. Ahmad, M. W., Reynolds, J., & Rezgui, Y. (2018). Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees. Journal of Cleaner Production, 203, 810–821. https://doi.org/10.1016/J.JCLEPRO.2018.08.207
4. Ahmed, T., Kumar, P., & Mottet, L. (2021). Natural ventilation in warm climates: The challenges of thermal comfort, heatwave resilience and indoor air quality. Renewable and Sustainable Energy Reviews, 138, 110669. https://doi.org/10.1016/J.RSER.2020.110669
5. Aryal, A., & Becerik-Gerber, B. (2020). Thermal comfort modeling when personalized comfort systems are in use: Comparison of sensing and learning methods. Building and Environment, 185, 107316. https://doi.org/10.1016/J.BUILDENV.2020.107316
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