Data-driven approach to develop prediction model for outdoor thermal comfort using optimized tree-type algorithms

[1]  Jaehyun Lee,et al.  Learning-driven construction productivity prediction for prefabricated external insulation wall system , 2022, Automation in Construction.

[2]  R. Ooka,et al.  Bayesian prediction model of thermally satisfied occupants considering stochasticity due to inter- and intra-individual thermal sensation variations , 2022, Journal of Building Engineering.

[3]  Pengyuan Shen,et al.  A review of studies and modelling of solar radiation on human thermal comfort in outdoor environment , 2022, Building and Environment.

[4]  Jaehyun Lee,et al.  Exploring Fatalities and Injuries in Construction by Considering Thermal Comfort Using Uncertainty and Relative Importance Analysis , 2021, International journal of environmental research and public health.

[5]  Baizhan Li,et al.  Individual thermal comfort prediction using classification tree model based on physiological parameters and thermal history in winter , 2021, Building Simulation.

[6]  D. Lai,et al.  A machine learning approach to predict outdoor thermal comfort using local skin temperatures , 2020 .

[7]  Cho Kwong Charlie Lam,et al.  Outdoor thermal comfort assessment: A review on thermal comfort research in Australia , 2020, Building and Environment.

[8]  Wim Zeiler,et al.  Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants' heating behavior. , 2020, Applied ergonomics.

[9]  R. de Dear,et al.  A review of adaptive thermal comfort research since 1998 , 2020 .

[10]  Xu Zhang,et al.  Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database , 2020 .

[11]  A. Olabi,et al.  Data-driven and numerical approaches to predict thermal comfort in traditional courtyards , 2020 .

[12]  Sangyoon Chin,et al.  Machine learning predictive model based on national data for fatal accidents of construction workers , 2020 .

[13]  Heng Li,et al.  Data mining approach to construction productivity prediction for cutter suction dredgers , 2019, Automation in Construction.

[14]  Nicola Baldo,et al.  Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation , 2019, Applied Sciences.

[15]  Bálint Baranyai,et al.  An overview of microclimate tools for predicting the thermal comfort, meteorological parameters and design strategies in outdoor spaces , 2019, Pollack Periodica.

[16]  Gail Brager,et al.  Analysis of the accuracy on PMV – PPD model using the ASHRAE Global Thermal Comfort Database II , 2019, Building and Environment.

[17]  D. Lai,et al.  An ordered probability model for predicting outdoor thermal comfort , 2018, Energy and Buildings.

[18]  M. Mahdavinejad,et al.  Seasonal differences of subjective thermal sensation and neutral temperature in an outdoor shaded space in Tehran, Iran , 2018 .

[19]  Joyce Kim,et al.  Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning , 2018 .

[20]  Taehoon Hong,et al.  An integrated psychological response score of the occupants based on their activities and the indoor environmental quality condition changes , 2017 .

[21]  D. Lai,et al.  Modelling dynamic thermal sensation of human subjects in outdoor environments , 2017 .

[22]  Dongwoo Yeom,et al.  Study of data-driven thermal sensation prediction model as a function of local body skin temperatures in a built environment , 2017 .

[23]  Yufei Xia,et al.  A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring , 2017, Expert Syst. Appl..

[24]  J. Kleissl,et al.  Predicting outdoor thermal comfort in urban environments: A 3D numerical model for standard effective temperature , 2017 .

[25]  Jean-Louis Scartezzini,et al.  Outdoor human comfort and thermal stress: A comprehensive review on models and standards , 2016 .

[26]  Jianlei Niu,et al.  Combining measured thermal parameters and simulated wind velocity to predict outdoor thermal comfort , 2016 .

[27]  Sanyam Shukla,et al.  Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification , 2016, 2016 IEEE 6th International Conference on Advanced Computing (IACC).

[28]  Erwan Scornet,et al.  A random forest guided tour , 2015, TEST.

[29]  Andreas Matzarakis,et al.  Outdoor thermal comfort characteristics in the hot and humid region from a gender perspective , 2014, International Journal of Biometeorology.

[30]  Dusan Fiala,et al.  Predicting urban outdoor thermal comfort by the Universal Thermal Climate Index UTCI—a case study in Southern Brazil , 2012, International Journal of Biometeorology.

[31]  Richard de Dear,et al.  Effect of thermal adaptation on seasonal outdoor thermal comfort , 2011 .

[32]  Cinzia Buratti,et al.  Adaptive analysis of thermal comfort in university classrooms: Correlation between experimental data and mathematical models , 2009 .

[33]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[34]  Jaemin Jeong,et al.  Quantitative Risk Evaluation of Fatal Incidents in Construction Based on Frequency and Probability Analysis , 2022, Journal of Management in Engineering.

[35]  Dayi Lai,et al.  Thermal comfort models and their developments: A review , 2021 .

[36]  J. van Hoof.  Forty years of Fanger's model of thermal comfort: comfort for all? , 2008, Indoor air.