Explainable machine-learning surrogates for sun-patch-driven indoor thermal comfort

Keyphrases

• Ankle 100%
• Capture System 33%
• Challenging Targets 33%
• Data-driven Surrogate Model 33%
• Direct Solar Radiation 33%
• Explainable Machine Learning 100%
• Floor System 33%
• Global Comfort 33%
• Head Temperature 33%
• Humidity 33%
• Hydronic Floor 33%
• Impact Analysis 33%
• Indoor Thermal Comfort 100%
• Irradiance 33%
• Local Overheating 66%
• Local Temperature 66%
• Local Thermal Comfort 33%
• Machine Learning Algorithms 33%
• Machine Learning Surrogate 100%
• Mismatch Phenomenon 33%
• Model Regression 33%
• Multiple Kernels 33%
• Neutral State 33%
• Objective Monitoring 33%
• Operative Temperature 33%
• Overheating Risk 33%
• Patch Management 33%
• Physical Drivers 33%
• Prediction Strategy 33%
• Principal Coordinate Analysis (PCoA) 33%
• Radiant Floor System 33%
• Radiative Interactions 33%
• SHAP Analysis 33%
• Short-term Evolution 33%
• Solar Exposure 33%
• Steady-state Model 33%
• Sun Patch 100%
• System Dynamics 33%
• Test Chamber 33%
• Thermal Asymmetry 33%
• Thermal Comfort 33%
• Thermal Sensation 33%
• Tree Ensembles 33%
• Tree Kernel 33%
• Tree-based 33%

Engineering

• Capture System 33%
• Component Analysis 33%
• Direct Solar Radiation 33%
• Explainable Artificial Intelligence 100%
• Floor System 66%
• Irradiance 33%
• Machine Learning Algorithm 33%
• Principal Component 33%
• Steady-State Model 33%
• Surrogate Model 33%
• Systems Dynamics 33%
• Test Chamber 33%
• Test Period 33%
• Thermal Comfort 100%