Abstract
This paper presents the working process for predicting thermal comfort using a data-driven model with Random Forest. The proposed model is tested considering the ASHRAE Thermal Comfort Database II, on which the results are based. Such a database comprises thermal comfort information worldwide and is developed to generate comfort prediction models based on additional new variables. The results of this study indicate that this approach has the potential to provide more accurate comfort predictions leading to more efficient and comfortable buildings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barbadilla-Martín, E., et al.: Sensitivity analysis in the prediction of thermal comfort: a machine learning-based approach. In: 16th International Conference on Industrial Engineering and Industrial Management XXVI Congreso de Ingeniería de Organización (2022)
Chaudhuri, T., et al.: Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology. Energy Build. 166, 391–406 (2018)
de Dear, R., et al.: A review of adaptive thermal comfort research since 1998. Energy Build. 214, 109893 (2020). https://doi.org/10.1016/j.enbuild.2020.109893
Földváry Ličina, V., et al.: Development of the ASHRAE global thermal comfort Database II. Build. Environ. 142, 502–512 (2018). https://doi.org/10.1016/j.buildenv.2018.06.022
Indraganti, M., Rao, K.D.: Effect of age, gender, economic group and tenure on thermal comfort: a field study in residential buildings in hot and dry climate with seasonal variations. Energy Build. 42(3), 273–281 (2010). https://doi.org/10.1016/j.enbuild.2009.09.003
Jin, L., Liu, T., Ma, J.: Modeling thermal sensation prediction using random forest classifier. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds.) LSMS/ICSEE -2021. CCIS, vol. 1469, pp. 552–561. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-7213-2_53
Kim, J., Schiavon, S., Brager, G.: Personal comfort models – a new paradigm in thermal comfort for occupant-centric environmental control. Build. Environ. 132, 114–124 (2017). https://doi.org/10.1016/j.buildenv.2018.01.023
Lu, S., et al.: Data-driven simulation of a thermal comfort-based temperature set-point control with ASHRAE RP884. Build. Environ. 156, 137–146 (2018). https://doi.org/10.1016/j.buildenv.2019.03.010
Luo, M., et al.: Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II. Energy Build. 210, 109776 (2020). https://doi.org/10.1016/j.enbuild.2020.109776
Zhang, F., Dear, R.D.: Impacts of demographic, contextual and interaction effects on thermal sensation—evidence from a global database. Build. Environ. 162, 106286 (2019). https://doi.org/10.1016/j.buildenv.2019.106286
Acknowledgements
This research has been funded through project SICODE (US-1380581) by the Junta de Andalucía and the project CONFORES (TED2021-130659B-I00) supported by the Ministry of Science and Innovation and financed by the European Union – NextGenerationEU.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aparicio-Ruiz, P., Barbadilla-Martín, E., Robles-Velasco, A., Ragel-Bonilla, J.C. (2024). Analysis of Thermal Comfort in Mediterranean Climate Buildings Using Random Forest. In: Bautista-Valhondo, J., Mateo-Doll, M., Lusa, A., Pastor-Moreno, R. (eds) Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023). CIO 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-031-57996-7_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-57996-7_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57995-0
Online ISBN: 978-3-031-57996-7
eBook Packages: EngineeringEngineering (R0)