Sustainable Cities and Society · 2026
Automated Urban Energy Assessment: From Thermal Flyover to AI-Driven Retrofit Prioritization for Sustainable Cities.
Uses Thermal-SAM to segment mid-wave infrared flyovers into building patches, pairs ring and context thermal features with EPC-derived EUI benchmarks, and shows thermal data is most useful for triaging anomalous high-EUI or EPC-inconsistent buildings for follow-up audits rather than routine direct EUI regression.
DOI
Hongshan Guo, Sebastiano Anselmo, Maria Ferrara, Shuai Niu, Binlin Chi, Xuchen Wang
energyclimate
Building and Environment · 2026
From Seven Points to Probabilities: Ordinal Learning for Risk-Aware Thermal Comfort Prediction.
Reframes thermal sensation prediction as an ordinal learning problem with calibrated probabilities for risk-aware comfort decisions. The model yields confidence-aware outputs that are more useful for control and design decisions under uncertainty than a flat seven-point label alone.
DOI
Hongshan Guo, Dorit Aviv
thermal comfortcontrol
Energy and AI · 2026
Toward Smarter HVAC Control: Machine Learning Reveals Hidden Drivers in Thermal Comfort Databases.
Shows how missing-data policy reshapes feature sensitivity rankings and supports MRT-aware, occupant-centric HVAC control. The paper makes clear that preprocessing decisions materially affect what machine-learning models appear to learn from thermal comfort databases.
DOI
Hongshan Guo, Ilaria Pigliautile, Yu Chang, Qingyao Qiao, Yichun Li
thermal comfortcontrolenergy
Energy and Buildings · 2026
Experimental Study on Gender Differences in Thermal Comfort and Physiological Responses in Fan-Assisted Cooling Environments.
Finds that gender differences become most pronounced under high-velocity cooling at lower temperatures, with implications for equitable fan-cooling design. By connecting subjective votes with skin-temperature responses, the study shows why mixed-mode cooling strategies should not assume uniform comfort response.
DOI
Chao Cen, Hongshan Guo, Lup Wai Chew, Nyuk Hien Wong
thermal comforthuman heat
Energy · 2026
Input Quality, Not Statistical Complexity, Determines Climate-Adapted Weather File Fidelity: A Causal Decomposition of Degree-Day Errors.
Shows that weather-file baseline quality dominates future demand bias, outperforming more complex workflows for climate-adapted energy projections. Rather than rewarding statistical complexity for its own sake, the paper identifies input fidelity as the main determinant of robust downstream building simulation.
DOI
Hongshan Guo, Kanxuan He
climateenergy
Energy and Buildings · 2026
Scenario-Conditioned Actual Meteorological Years (sAMY): A Stochastic Weather Generator Using Multi-Decadal Observations.
Develops a stochastic weather-generation workflow using multi-decadal observations to support scenario-conditioned building performance analysis under climate uncertainty.
DOI
Hongshan Guo, Kanxuan He
climateenergy
Energy and Buildings · 2026
Ventilation-Energy Trade-offs in Retrofitted Hong Kong Wet Markets.
Combines field diagnostics and cross-climate simulation to quantify ventilation, comfort, and energy trade-offs in retrofitted Hong Kong wet markets. It frames wet-market modernization as both a building-performance problem and a public-institution design question.
DOI
Hongshan Guo, Yu Chang, Yichun Li, Ying Zhou, Qingyao Qiao, Chun Yin Lai, Eric Schuldenfrei
ventilationenergyclimate
Energy and Buildings · 2025
Correcting the 120-Watt Assumption: Demographic-Aware Metabolic Rates for Energy Savings and Thermal Comfort Equity in Buildings.
Demonstrates that demographic-aware metabolic loads can cut HVAC energy use and reduce gender-based comfort bias relative to the legacy 120 W/person assumption. The work directly challenged a century-old default embedded in standards and helped motivate ASHRAE 1959-TRP.
DOI
Hongshan Guo, Ruiji Sun, Youmin Xu
human heatenergy
Energy and Buildings · 2026
A Co-Simulation Methodology for Integrating Data-Driven Thermal Sensation Models with Building Energy Control.
Integrates data-driven thermal sensation models with building control workflows to benchmark occupant-aware control across multiple climates. It shows how co-simulation can move comfort models from offline prediction into actionable control testing.
DOI
Hongshan Guo, Kanxuan He, Youmin Xu, Yue Lei
thermal comfortcontrolclimate
Building and Environment · 2025
Physics-Informed Neural Networks for Robust Thermal Comfort Prediction: Overcoming Data Quality Limitations Through Physiological Constraints.
Uses physiological constraints inside a neural model to improve robustness and interpretability in large-scale thermal comfort prediction. The approach treats building-comfort ML as a physically informed modeling problem rather than a purely statistical fitting exercise.
DOI
Hongshan Guo, Kanxuan He, Yongqiang Luo, Yu Chang
thermal comforthuman heat
Renewable and Sustainable Energy Reviews · 2025
A Data-Driven Qualitative Review of Thermal Comfort Studies: Bridging the Gap Between Western and Eastern Perspectives.
Reviews how comfort studies use personal, contextual, and PMV-related variables, highlighting gaps that limit cross-cultural comparison and model transfer. It also establishes a benchmark-oriented framing for comparing Eastern and Western comfort evidence more systematically.
DOI
Yu Chang, Hongshan Guo, Yichun Li, Ilaria Pigliautile, Binlin Chi
thermal comfortclimate