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A Thermal AnalysisOf P-Block
Presented by Owen Laroque
Contents
Background
Method of Analysis
The Zone
Results
The next step
Background
Statistical method
Box-Jenkins Feed-forward networks
Ideal conditions vs current
Method of analysis
Statistical Method
Thermodynamic and HVAC
Why is the thermodynamic approach better?
Statistical Method
Application of the Box-Jenkins model
Find an optimisation equation that accounts for: Air temperature
Air velocity
etc.
Compare optimised to current condition
𝑉𝑠 =𝑉𝑠𝑚𝑎𝑥 − 𝑉𝑠
𝑚𝑖𝑛
𝑉𝑚𝑎𝑥 − 𝑉𝑚𝑖𝑛𝑉 − 𝑉𝑚𝑖𝑛 + 𝑉𝑠
𝑚𝑖𝑛
𝑂 = 𝑤𝑃𝑀𝑉𝑃𝑀𝑉𝑠 − 𝑃𝑀𝑉
𝑃𝑀𝑉𝑠+ 𝑤𝐶
𝐶𝑠 − 𝐶
𝐶𝑠
Thermodynamic and HVAC approach
Understanding the application of principles
What does occupancy and out door conditions actually do?
Data based analysis
HVAC vs Statistical approach
Statistical analysis is focused on finding optimal conditions
HVAC is focused on what the operating conditions are and why
Zoning
Multiple computers
Screen wall
Ranging Occupancy
High volume over multiple levels
Occupancy
Before semester vs During semester
Low occupancy outside of class times
High volume of people when classes are on
Study area means there is generally people around
Electrical load
High load environment
Lots of lighting
Lots of computers
Screen wall
Projectors
Brisbane Climate
28oC – 35oC
High humidity
Next to Highway therefore poor air quality
Data Supply air temperature from air handling unit
Return air temperature from zone
Relative humidity in zone
Supply air temperature
Expected to rise and fall with office hours
Low during peak times
High outside of peak times
See cooling load rise and fall
Return air temperature
Expected to be relatively consistent and stable
Expected to clearly drop when system is turned on
See affects of occupancy
Humidity See clear dehumidification
Is an indication to whether the system is on or not
Temperature Difference
See rise and fall in cooling load
Used to calculate heat removed from the air in the zone Area under curve)
Q=mC∆T M and C are constant, ∆T is known
Low Occupancy
It was expected that there would be no change in operation depending on occupancy
Appears to be a three-day model that is in use for before semester and during weekends of semester (Friday – Sunday)
Much lower use of the system
Seemingly an strategy to save money
High Occupancy This is during the week of semester.
System was expected to be in use during office hours
System is actually used from early morning to mid-afternoon
Low vs High occupancy
For low occupancy there is approximately 5.91kWh of cooling over the 3 day pattern before semester
For high occupancy there is 3.67 kWh of cooling over one day during semester
Next steps
Conduct research on thermal comfort PMVs
See possible changes that can be made to operating conditions
Compare to benchmarks of other buildings