Enterprise AI Analysis
Review of Data-Driven Personal Thermal Comfort Modeling and Its Integration into Building Environment Control
This review systematically covers data-driven personal thermal comfort modeling, focusing on data collection methods (contact-based vs. non-contact), feature correlation, and advanced modeling techniques like machine learning (ML) and deep learning (DL). It highlights data scarcity solutions using transfer learning (TL) and discusses integrating these models into building environment control for improved comfort and energy efficiency, identifying current challenges and future directions.
Executive Impact & AI Readiness
Our analysis reveals the direct, quantifiable benefits of adopting AI-driven personal thermal comfort modeling within your enterprise:
0
Contact-Based Data Collection Rate
0
DL Model Average Accuracy (3-point TS)
0
R-LSTM Model Average Accuracy (TP)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Data Collection & Preprocessing
This section reviews the types and methods of data collection for personal thermal comfort modeling. It differentiates between contact-based and non-contact methods, discussing their respective advantages and limitations. Additionally, it covers data preprocessing techniques, especially correlation analysis and feature selection, crucial for improving model accuracy.
67.1%
of studies use contact-based data collection, indicating a preference for high precision despite direct contact
Enterprise Process Flow
Environmental Data
→
Physiological Data
→
Behavioral Data
→
Subjective Feedback
→
Data Preprocessing
→
Feature Selection
→
Model Training
| Aspect | Contact-Based | Non-Contact |
|---|---|---|
| Accuracy | High precision, detailed physiological signals | Influenced by environmental factors, orientation |
| Subject Experience | May affect behavior/psychological state | Good subject experience, ease of deployment |
| Typical Devices | Thermocouples, pulse oximeters | Thermal imaging devices, web cameras |
Modeling Methods (ML/DL)
This part delves into the application of Machine Learning (ML) and Deep Learning (DL) algorithms for personal thermal comfort modeling. It outlines common ML models like SVM, RF, and KNN, and highlights DL methods such as CNN and LSTM, emphasizing their strengths in handling complex, non-linear relationships and high-dimensional data.
64.6%
of reviewed literature adopts Machine Learning methods
DL-based Thermal Sensation Prediction (Zakka et al. [28])
A study by Zakka et al. [28] collected thermal imaging and TSV data from 10 subjects under various temperature conditions. They developed CNN-based models for 3-point and 7-point thermal sensation scales. The 3-point model achieved an average accuracy of 99.51%, while the 7-point model reached 89.90% accuracy, outperforming many existing models. This highlights the superior capability of DL in complex feature engineering and multi-sensor fusion for thermal comfort prediction.
| Feature | Machine Learning (ML) | Deep Learning (DL) |
|---|---|---|
| Common Models | SVM, RF, KNN | CNN, LSTM, Attention-ResNet |
| Data Scale | Smaller to medium datasets | Very large-scale data |
| Non-linear Relationships | Good, but less flexible for complex ones | Stronger flexibility, better generalization |
| Feature Extraction | Requires manual feature engineering | Automatic feature extraction from raw data |
Transfer Learning (TL) for Data Scarcity
This section explores how Transfer Learning (TL) addresses data scarcity issues in personal thermal comfort modeling. It describes various TL strategies like Model-Based (MBTL), Instance-Based (IBTL), and Feature-Based (FBTL) methods, and their effectiveness in improving model accuracy and generalization with limited target domain data.
Significant
improvement in prediction accuracy achieved with TL in data scarcity scenarios
Enterprise Process Flow
Source Domain Data (Public Datasets)
→
Pre-train Model (Source Task)
→
Transfer Knowledge
→
Target Domain Data (Limited)
→
Fine-tune Model (Target Task)
→
Improved PTCM
| Method | Description | Strengths |
|---|---|---|
| Model-Based (MBTL) | Pre-trains model on source data, fine-tunes on target data. | Higher efficiency with larger target data, extracts deep features. |
| Instance-Based (IBTL) | Selects similar samples from source to supplement target data. | Most significant improvement with very limited on-site data. |
| Feature-Based (FBTL) | Projects source and target domains into shared low-dimensional space. | Outperforms MBTL/IBTL when data distribution differs significantly. |
Integration into Building Control
This part discusses the integration of PTCMs into building environment control systems. It highlights the potential for personalized comfort and energy savings, outlining the typical integration framework and current challenges, such as hardware requirements, 'lab-to-field' gaps, and protocol compatibility.
50%
reduction in thermal stabilization time and HVAC energy consumption (Li et al. [140])
Enterprise Process Flow
Sensors (Environmental, Wearable, Camera)
→
Network (Data Transmission)
→
Controller (PTCM, Control Strategy)
→
Actuators (HVAC, PCS)
→
Building Environment
Personalized HVAC Control (Li et al. [140])
Li et al. [140] developed a non-contact human thermal sensation monitoring and regulation system. They used facial thermography and the YOLO algorithm to recognize thermal sensitive regions. An RF-based personal TS model was established, combined with the PMV model in an ensemble way to handle transient detection interruptions. A fuzzy controller was designed to regulate HVAC. Field experiments showed the ensemble TS prediction model achieved 96.07% accuracy (5-point scale) and reduced thermal stabilization time and HVAC energy consumption by 50% compared to fixed setpoint control.
Advanced ROI Calculator
Utilize our ROI Calculator to estimate the potential cost savings and efficiency gains for your organization by implementing AI-driven personal thermal comfort systems. Adjust parameters like industry, number of employees, average discomfort hours, and hourly labor cost to see immediate, projected returns.
Projected Annual Savings
$0
Employee Hours Reclaimed Annually
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Your Implementation Roadmap
Deploying AI for personalized thermal comfort is a strategic journey. Here’s a typical phased approach to ensure a successful transition and optimal results:
Phase 1: Data Audit & Sensor Deployment
Assess existing infrastructure, identify data sources, and deploy necessary environmental, physiological, and behavioral sensors. Focus on non-contact where feasible to ensure minimal occupant disruption. Establish data collection protocols.
Phase 2: Model Training & Transfer Learning
Utilize collected data to train initial personal thermal comfort models. Implement transfer learning strategies, leveraging public datasets or pre-existing models, to address data scarcity and enhance prediction accuracy for specific building contexts.
Phase 3: Integration & Control Strategy Development
Integrate PTCMs with building control systems (HVAC, PCS). Develop and implement advanced control algorithms (e.g., MPC, fuzzy logic, reinforcement learning) that use personalized comfort predictions to optimize environmental settings.
Phase 4: Validation, Optimization & Scaling
Conduct long-term field validations to assess the system's robustness, energy efficiency, and occupant satisfaction. Continuously optimize models with online learning and adaptive mechanisms. Plan for spatial scalability across different zones and buildings.
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