A dynamic convergence algorithm for thermal comfort modelling

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Workshop on Cognitive and Computer Vision Assisted Systems for Energy Awareness and Behavior Analysis 25 Sept 2019

Technology

A Dynamic Convergence Algorithm for
Thermal Comfort Modelling
Authors:
A.Dimara,C.Timplalexis, S.Krinidis, D.Tzovaras
Workshop on Cognitive and Computer
Vision Assisted Systems for Energy
Awareness and Behavior Analysis
25 Sept 2019
ICVS
Thessaloniki

Energy saving but with comfortable approaches
• Alternation in energy consumption behaviour by
energy saving.
• Τhe energy saving must be achieved with.
comfortable approaches for residents.
• Subjective comfort levels of the occupants.
2

Thermal comfort
3
ASHRAE thermal
comfort scale
Human satisfactory perception
of the indoor environment
• Indoor temperature(Ta)
• Indoor humidity (RH)
Environmental
factors
• Clothing insulation (Icl)
• Metabolic rate (M)
Personal
factors
Fanger’s PMV equation

Personal factors
4
Clothing insulation
Metabolic rate
Clothing insulation &
Metabolic rate
Different estimation

How to estimate thermal comfort
5
Indoor
temperature
sensor
Indoor
Humidity
sensor
Environmental factors Personal factors
Clothing insulationMetabolic rate
ASHRAE
table
Questionnaire
Thermal
manikin
ASHRAE
table Questionnaire
Wearable
metabolic
devices

Progressive update of personal factors
6
 Initial assumption or
personal factors from
ASHRAE tables.
 Estimate real values
from PMV feedback.
 Indoor temperature
affects the way we
dress and act in indoor
spaces.
 Estimate Icl, M from
Indoor temperature.

Relationship between clothing
insulation and Indoor temperature
8

Comparison of our approach and
Morgan's study
9

Relationship between metabolic rate
insulation and Indoor temperature
10

Performance test of the equations
Thermal comfort estimation
11

Conclusions
 Thermal comfort is affected by indoor micro climatic parameters but also by
personal psychological estimation a that model concentrates more on the
personal factors is needed for the PMV computation.
 The non-linear regression relationship between clothing insulation and
indoor temperatures advocates that indoor temperature is an essential
factor of clothing worn inside buildings.
 The non-linear regression relationship between metabolic rate and indoor
temperatures also reveals that temperature is a dominant component of
metabolic rate.
 If a non-linear regression model will be fitted individually and distinctly for
every user the relationship between indoor temperature and personal
factors will grow stronger.

12th International Conference on Computer
Vision Systems | 23-25 Sept 2019 |
Thessaloniki, Greece
13
Thank you!
Any Questions?
Contributors:
A.Dimara, C.Timplalexis, S.Krinidis, D.Tzovaras
Workshop on Cognitive and Computer
Vision Assisted Systems for Energy
Awareness and Behavior Analysis
25 Sept 2019

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A dynamic convergence algorithm for thermal comfort modelling

1. A Dynamic Convergence Algorithm for Thermal Comfort Modelling Authors: A.Dimara,C.Timplalexis, S.Krinidis, D.Tzovaras Workshop on Cognitive and Computer Vision Assisted Systems for Energy Awareness and Behavior Analysis 25 Sept 2019 ICVS Thessaloniki

2. Energy saving but with comfortable approaches • Alternation in energy consumption behaviour by energy saving. • Τhe energy saving must be achieved with. comfortable approaches for residents. • Subjective comfort levels of the occupants. 2

3. Thermal comfort 3 ASHRAE thermal comfort scale Human satisfactory perception of the indoor environment • Indoor temperature(Ta) • Indoor humidity (RH) Environmental factors • Clothing insulation (Icl) • Metabolic rate (M) Personal factors Fanger’s PMV equation

4. Personal factors 4 Clothing insulation Metabolic rate Clothing insulation & Metabolic rate Different estimation

5. How to estimate thermal comfort 5 Indoor temperature sensor Indoor Humidity sensor Environmental factors Personal factors Clothing insulationMetabolic rate ASHRAE table Questionnaire Thermal manikin ASHRAE table Questionnaire Wearable metabolic devices

6. Progressive update of personal factors 6  Initial assumption or personal factors from ASHRAE tables.  Estimate real values from PMV feedback.  Indoor temperature affects the way we dress and act in indoor spaces.  Estimate Icl, M from Indoor temperature.

7. Data collection 7

8. Relationship between clothing insulation and Indoor temperature 8

9. Comparison of our approach and Morgan's study 9

10. Relationship between metabolic rate insulation and Indoor temperature 10

11. Performance test of the equations Thermal comfort estimation 11

12. Conclusions  Thermal comfort is affected by indoor micro climatic parameters but also by personal psychological estimation a that model concentrates more on the personal factors is needed for the PMV computation.  The non-linear regression relationship between clothing insulation and indoor temperatures advocates that indoor temperature is an essential factor of clothing worn inside buildings.  The non-linear regression relationship between metabolic rate and indoor temperatures also reveals that temperature is a dominant component of metabolic rate.  If a non-linear regression model will be fitted individually and distinctly for every user the relationship between indoor temperature and personal factors will grow stronger.

13. 12th International Conference on Computer Vision Systems | 23-25 Sept 2019 | Thessaloniki, Greece 13 Thank you! Any Questions? Contributors: A.Dimara, C.Timplalexis, S.Krinidis, D.Tzovaras Workshop on Cognitive and Computer Vision Assisted Systems for Energy Awareness and Behavior Analysis 25 Sept 2019

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