Thermal Comfort Scenarios

• Thermal comfort scenarios are formalized models that combine environmental, physiological, behavioral, and morphological parameters to assess human thermal well-being.
• They leverage parametric grids, high-resolution sensor data, and simulation techniques to inform occupant-centric HVAC control, urban heat-stress mitigation, and personalized comfort systems.
• Key metrics like UTCI, PMV, and PET drive these models, enabling adaptive, energy-efficient, and context-specific thermal management across diverse settings.

Thermal comfort scenarios are formalized models or empirical configurations that represent the interplay between environmental, physiological, behavioral, and morphological factors affecting the sensation of thermal well-being for humans in built or outdoor environments. These scenarios are used to diagnose, predict, optimize, and control thermal comfort at varying spatial, temporal, and organizational scales. The definition, modeling, and evaluation of these scenarios encompass both human-centered and environment-centered approaches, leveraging simulation, sensing, physiological signals, and advanced optimization or learning frameworks. Scenarios may be used for occupant-centric HVAC control, outdoor microclimate interventions, energy-efficiency trade-off studies, clothing recommendations, group consensus algorithms in shared spaces, and artificial intelligence benchmarking.

1. Parametric Formulation of Thermal Comfort Scenarios

Thermal comfort scenarios are parameterized by multidimensional combinations of environmental, morphological, behavioral, and individual factors. Key variables include:

• Environmental drivers: air temperature ($T_a$), mean radiant temperature ($T_{mrt}$), relative humidity (RH), air velocity ($v$), solar and longwave fluxes, and their spatiotemporal distributions.
• Individual factors: clothing insulation ($I_{cl}$), metabolic rate (MET), physiological states (e.g., heart rate, skin temperature), as well as psychophysical and affective attributes.
• Urban and building morphology: green surface fraction, impervious surface fraction, sky-view factor, building density, tree canopy fraction, and other urban canopy parameters.

Scenario construction often proceeds as a sweep or grid over prescribed ranges for these parameters. For instance, TCEval uses a $9 \times 5 \times 3 \times 3$ environmental factor grid, resulting in 405 distinct environmental "cells," each paired with occupant profiles varying $I_{cl}$, MET, and "persona" attributes (Li, 29 Dec 2025). Outdoor city-scale frameworks (e.g., WRF-UCM-SOLWEIG) exploit high-resolution spatial fields (10 m) to assemble pixel-wise scenario maps (Ding et al., 2023). Dynamic laboratory or field protocols (e.g., ramp and step changes in temperature) are used to capture transient responses (Ashrafi et al., 2022, Colley et al., 2022).

2. Scenario Types and Application Domains

Several canonical scenario typologies are distinguished across research:

• Outdoor urban heat-stress scenarios: Quantify intra-day, spatial, and morphological modulations in perceived temperature and UTCI due to features like impervious surfaces or tree canopy. Example: Guangzhou during heat wave, diurnal UTCI > 42°C midday (Ding et al., 2023); probabilistic PET maps in Singapore showing up to 65% acceptable comfort in green zones, only 30% in dense build-up (Chen et al., 11 Sep 2025).
• Indoor HVAC and participatory comfort control: Configurations for optimizing shared-space comfort, such as open-plan offices, using parametric HVAC setpoints, real-time sensing, group consensus metrics, and energy constraints (Tekler et al., 2023, Wang et al., 2024, Lopez et al., 2020, Ihianle et al., 2022).
• Vehicle and microspace scenarios: Address dynamic, non-uniform, and occupant-specific conditions found in automotive cabins or enclosed pods, leveraging multimodal sensor data and deep sequence models (Colley et al., 2022).
• Personal comfort system scenarios: Quantify the effects of localized interventions (neck coolers, in-ear IR heating) on subjective and physiological responses under laboratory or semi-field conditions (Zitz et al., 3 Jun 2025, Nkurikiyeyezu et al., 2019).
• Underground/sheltered refuge scenarios: Detailed partitioning of periods (e.g., early-morning, cooling, heat-stressing) to analyze temporal regimes in high thermal-mass environments (Chen et al., 22 Jul 2025).

Scenario planning is also applied for AI benchmarking and agent-based reasoning, where virtual agents select attire and forecast comfort across gridded environmental-personal combinations (Li, 29 Dec 2025).

3. Metrics and Modeling Frameworks

Major thermal comfort metrics operationalize scenario evaluation:

• Universal Thermal Climate Index (UTCI): Calculated as $$\mathrm{UTCI}=f(T_a,\mathrm{RH},\mathrm{WS},T_{mrt})$$ via high-order polynomial, grounded in human physiological response models (Ding et al., 2023).
• Predicted Mean Vote (PMV): Fanger's steady-state heat-balance index,

$$PMV = \left[0.303\,e^{-0.036\,M} + 0.028\right] \Bigl[ \ldots \Bigr]$$

with explicit dependence on $T_a$, $T_r$, $v$, RH, $I_{cl}$, and $M$; core to personalized and adaptive control (Li, 29 Dec 2025, Kim et al., 1 May 2025, Tekler et al., 2023).

• Physiological Equivalent Temperature (PET): Energy-balance–derived thermal equivalent for outdoor scenarios, emphasizing radiative and airflow constraints (Chen et al., 11 Sep 2025, Chen et al., 22 Jul 2025).
• Sensation of Discomfort Index (SDI, Editor’s term): Combines environmental index (e.g., DI) with per-user intrinsic offset and wearable device correction, enabling full-floor, all-user comfort optimization (Lopez et al., 2020).

Frameworks such as WRF-UCM-SOLWEIG enable multi-scale coupling (city-scale meteorology down to micro-scale $T_{mrt}$) for resolved intra-urban scenario analysis (Ding et al., 2023). Gappy POD (Kim et al., 1 May 2025) or spatio-temporal jump clustering (Cortese et al., 2024) reconstruct fields and regimes for occupant-oriented mapping. Active learning and committee-based acquisition select the most informative scenario–label pairs to minimize annotation effort in human-in-the-loop deployments (Tekler et al., 2023).

4. Scenario Evaluation, Adaptation, and Decision-Making

Scenarios serve as the backbone for optimization and adaptive control in real-world systems:

• Scenario-adaptive control logic: Central setpoint and wearable PCS settings jointly tuned to keep every $SDI_i$ within maximum thermal comfort range, minimizing total energy, via interval search or consensus optimization (Lopez et al., 2020, Wang et al., 2024, Ihianle et al., 2022).
• Group and individual comfort trade-off: Scenarios define choice of aggregation metric—median PMV, weighted average emphasizing high-discomfort cases, or median+MAD for bias correction—driving zone-level control decisions (Kim et al., 1 May 2025, Wang et al., 2024).
• Physiological feedback and personal adaptation: Physiological scenario response (e.g., heart rate variability state, skin temperature) indexes interventions—personal fans, neck coolers, PCS—enabling tight loop-controlled microclimate optimization (Nkurikiyeyezu et al., 2019, Nkurikiyeyezu et al., 2020, Ashrafi et al., 2022).
• Temporal and spatial regime recognition: Spatio-temporal regime scenarios (Cool/Neutral/Hot) allow clustering of weather-station fields over time for real-time alerting, long-term design, and public health (Cortese et al., 2024).
• AI/agent scenario evaluation: In TCEval, LLM agent scenario responses are evaluated against human-labeled PMV/thermal class to quantify their causal reasoning and adaptive decision-making under varying environmental–personal configurations (Li, 29 Dec 2025).

Robust scenario-based control frameworks yield significant reductions in annotation effort (31%, (Tekler et al., 2023)), energy (up to 30%, (Lopez et al., 2020)), and maximize comfort acceptability (>98%).

5. Urban, Morphological, and Microclimatic Scenario Impacts

Scenario-driven analysis reveals dominant levers and actionable interventions in urban and microclimatic contexts:

• Vegetation and Tree Canopy: Embedding ≥40% tree canopy within 500 m urban blocks reduces UTCI by 1.5–2.0 K at the diurnal peak. Green corridors create cooling "blue patches," directly correlating with improved comfort metrics (Ding et al., 2023, Chen et al., 11 Sep 2025).
• Impervious Surface and Building Density: Areas with high impervious surface fraction (ISF) exhibit strongly elevated heat-stress. Each 10% ISF reduction cuts UTCI by ≈0.5 K; building density up to 30% increases midday UTCI by 2.4–2.6 K, with diminishing returns beyond (Ding et al., 2023).
• Underground and Sheltered Scenarios: Underground spaces, due to high thermal mass and shading, provide the lowest PET during extreme heat, especially in the 10:00–19:00 "cooling period." Metabolic rate varies PET by 7 K, air velocity by 2 K, while sharp transitions isolate optimal periods for refuge (Chen et al., 22 Jul 2025).
• Radiation and Clothing Scenarios: Anisotropic shortwave irradiation is a primary cause of localized discomfort outdoors; accurate scenario modeling must resolve zone-by-zone radiative loads, including clothing transmissivity, for actual exposure (Sadeghi et al., 6 Feb 2025).
• Temporal and Weather-Driven Scenarios: Mixed spatial-temporal clusters reveal persistent urban comfort regimes, indicating periods of highest risk, spatial hot spots, and avenues for targeted intervention (Cortese et al., 2024).

6. Measurement, Modeling Tools, and Data Sources in Scenario Construction

Thermal comfort scenarios are underpinned by a rich methodological toolkit:

• Environmental sensors and imaging: High-resolution environmental fields constructed from dense sensor networks (e.g., 12 boundary sensors + 3 internal points, (Kim et al., 1 May 2025)), or through low-cost thermal imaging and visual cameras for real-time, per-user skin and facial temperature assessment (Ashrafi et al., 2022).
• Machine learning and surrogate modeling: Random forests, XGBoost, LSTM-RNNs, and committee approaches support both rapid scenario emulation for control (PET surrogates achieving RMSE ≈ 0.02 K (Chen et al., 22 Jul 2025), AL-enabled model performance at 69% labelling effort (Tekler et al., 2023)), and prediction from physiological proxies.
• Calibration and personalization: Personalized calibration, e.g., 400 HRV samples yielding 96% accuracy on individual models (Nkurikiyeyezu et al., 2019), or peruser bias estimation for SDI, enables full exploitation of scenario specificity.
• Scenario datasets: Large, annotated datasets comprising indoor and in-vehicle time series (31-variable, (Colley et al., 2022)) or city-scale morphological fields support benchmarking and cross-system transfer.

7. Implications and Limitations

Thermal comfort scenarios enable both theoretical advances and practical system deployment:

• Greater personalization and spatial/temporal granularity of scenarios foster robust, energy-efficient, and user-centered control strategies, but at the cost of increased data, computational, and sensing complexity.
• Scenario-based AI benchmarking (e.g., TCEval) exposes limitations in current large-language-model causal reasoning under high-dimensional physical environments (with <31% exact PMV alignment to humans, despite 50–57% directional consistency) (Li, 29 Dec 2025).
• Physiological and behavioral heterogeneity, spatial field errors, and the limits of group compromise define the bounds of comfort scenario predictiveness; ongoing research addresses learning drift, data privacy, fully-passive inference, and missing data imputation.

By formalizing and leveraging high-dimensional, empirically-grounded, and occupant-aware thermal comfort scenarios, the state of the art increasingly supports adaptive, fair, and optimized microclimate management across diverse built and outdoor environments (Ding et al., 2023, Chen et al., 11 Sep 2025, Cortese et al., 2024, Tekler et al., 2023, Li, 29 Dec 2025).