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Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users' Feedback, IoT and Machine
Francesco Salamone1, Lorenzo Belussi2, Cristian Currò3
1ITC-CNR, Construction Technologies Institute-National Research Council of Italy, Lombardia St., 49-20098 San Giuliano M.se, Italy. francesco.salamone@itc.cnr.it.
Sensors (Basel, Switzerland)
|May 19, 2018
Summary
This study explores IoT and machine learning for assessing thermal comfort in offices. It aims to improve user satisfaction by considering personal factors alongside environmental data.
Area of Science:
- Building Performance Assessment
- Energy Efficiency
- Human-Building Interaction
Background:
- Traditional thermal comfort methods (PMV, PPD) assume steady-state conditions.
- The adaptive approach considers outdoor climate but can be expanded.
- Integrating endogenous user variables offers a more holistic perspective on thermal perception.
Purpose of the Study:
- To investigate the reliability of IoT-based solutions and machine learning for thermal comfort assessment.
- To develop a replicable framework for evaluating and enhancing user thermal satisfaction.
- To explore the integration of nearable/wearable technology with advanced algorithms.
Main Methods:
- In-field investigation in real office environments with eight participants.
- Application of parametric models for thermal comfort assessment.
- Utilization of IoT solutions for monitoring environmental and user parameters.
- Employing the Classification and Regression Trees (CART) machine learning method.
Main Results:
- Parametric models assessed thermal comfort conditions.
- IoT devices effectively monitored environmental and personal variables.
- The CART method successfully predicted user profiles and thermal perception.
- Demonstrated the potential of integrated IoT and ML for personalized thermal comfort.
Conclusions:
- IoT-based solutions combined with machine learning offer a reliable framework for thermal comfort assessment.
- This approach allows for a more personalized and improved user thermal satisfaction.
- The developed framework is replicable for diverse office environments.
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