Relationship between building occupancy and HVAC system loads for energy efficiency. https://www.researchgate.net/profile/Zheng_Yang12

1. How Does Building Occupancy Influence
Energy Efficiency of HVC Systems
Zheng Yang
Zheng Yang
www.zhengyang.me
PhD Candidate Viterbi Fellow
Innovation in Integrated Informatics Lab (i-LAB.usc.edu)
Department of Civil and Environmental Engineering
University of Southern California

2. Commercial Building Energy Consumption
Commercial
Buildings
Figure. Building Energy Consumption
(IEA 2014; DOE 2014)
Figure. U.S. Energy Consumption in 2013
(IEA 2014; DOE 2014)
80%
Figure. Commercial Building Energy
(DOE 2013, 2014)
HVAC: Heating, Ventilation and Air Conditioning
Image Source: Regulvar Control

3. HVAC Energy Efficiency
Inefficiency: 90% of HVAC systems are inefficient (EIA 2012, Carbon Trust 2012, UNEP 2013)
Energy required on the demand side;
Energy consumed on the supply side;
Building Physical Characteristics
Effects have decreased (Guerra-Santin 2010)
Governments have introduced regulations and policies
New Technology and Systems
Infeasible and unpredictable (USGBC 2010)
Existing buildings have already installed HVAC systems
Demand driven Control
React to actual demands (CIBSE 2012)
Based on real space loads to keep desired conditions
Occupants Control Policies
Actual Demands
Difference:

4. Figure. The importance of occupant in HVAC energy consumption
Occupancy and HVAC
Occupant activity, control preferences and personal information

5. Heating/Cooling, Terminal and Setpoint
Terminals Setpoint
Demands Medium
Heating / Cooling
Supply side
Loads
Control unit and interface
Temperature range (deadband) Thermostat
Primary parameter
Until 2011, 90% of actively conditioned buildings Thermostat Setpoint
(ASHRAE 2012, Johnson Control 2012)
SETPOINT
HVAC
Response
Thermal
Environment
Occupant

6. Problem Analysis
• Stochastic in nature and has variety;
• Random variations and variant transitions;
• Heterogeneous and even distinct;
Why?
Systematic research for analyzing the influences of occupancy on HVAC energy efficiency
- Occupancy Transitions - Occupancy Variations - Occupancy Heterogeneity
How?
• Not fully run HVAC system in vacant zones;
• Allow temperature to float within a certain range (Setback);
• Substantial energy savings have been reported;

7. Test Bed Building
Test bed building in University of Southern California
Ambient Sensing based
Cross-Space Occupancy Modeling
(Zheng et al. 2013, 2014)
Initial Energy Modeling
Sensitivity Analysis
Parametric Comparison
Parameter Estimation
Base Modeling
Discrepancy Analysis
Discrepancy Minimization
Calibrated Energy Model
Non-observable Parameter Recognition and Range
Ranking (Level 1) Ranking (Macro Level 2)
Estimable Parameter
Adjustable Parameter
Multi-objective Programming
Regression-fitting
Estimable Evidence
Observable Evidence
Significant Parameters
Distribution Analysis
Random Samples
Parameter Range and Condition
Semi-calibrated Model
Actual Energy Data Input
Energy Discrepancy Explanation
Actual Energy Data Input
Calibration Evaluation
Default and Autosized
Insignificant Parameters
...
Multi-level building energy
model calibration
(Zheng et al. 2014)

8. Occupancy Transitions
Occupied period – Setpoint; Unoccupied period – Setback;
Setpoint Float Setback Reconditioning
Effective EffectiveIneffective
Occupied Unoccupied Occupied
Time
Figure. Deviation between occupancy and effective loads
Occupied/Unoccupied Transitions ≠ Effective/Ineffective Loads Transitions
A portion of the loads during unoccupied periods = Effective loads

9. Simulation Results
• The darker the color is, the more energy reduction and less conditioning miss are achieved.
• energy efficiency is expressed as a weighted sum of the two gray maps (50% for each)
• Occupancy transitions have significant influences on the HVAC energy efficiency (4% to 21%)
Occupancy Transitions and HVAC Energy Efficiency
1 2 3 4 5 6 7 8
0
5
10
15
20
25
30
35
Setpoint/SetbackSchedule(Min)
Setpoint/Setback Distance (K)
Energy Reduction (%)
1 2 3 4 5 6 7 8
0
5
10
15
20
25
30
35
Setpoint/SetbackSchedule(Min)
Setpoint/Setback Distance (K)
Conditioning Miss (%)

10. Stochastic Occupancy Variations ~ effective heating/cooling loads
Degree of
Occupancy Variation
Deterministic
Stochastic
Long-term Occupancy
Habitual patterns
Represents typical effective loads
Real-time Occupancy
Occupancy status for specific time
Represents instant effective loads
Occupancy Variations
Euclidean distance between the actual daily
occupancy versus the occupancy profile

11. Deviation of daily real-time occupancy
From occupancy profile
Calculate the Daily average
variation degree
Simulation Results
Daily energy reduction and
conditioning miss
Occupancy based control
(15 minutes and 78F)
Occupancy Variations and HVAC Energy Efficiency
• A Negative linear relationship between the occupancy variation and HVAC energy efficiency.
• HVAC energy efficiency for each specific day is significantly influenced by the variation of
occupancy for that day (from 3% to 24%)

12. Presence
Probability
Time
Presence
Probability
Time
Presence
Probability
Time
Presence
Probability
Time
Occupancy
Heterogeneous occupancy
Effective Loads
Heterogeneous load distribution
Occupancy Heterogeneity
Long-term Occupancy
(Occupancy Profile)
Load Redistribution
Hierarchical and Conditional
Occupant Reassignment
(Zheng et al. 2014, 2015)
Predefined constrains
(e.g. room size)
Capacity constrains
(e.g. number of rooms)
Geometry constrains
(e.g. zone adjacency)
For a specific HVAC
control policy
Possible Energy
Efficiency Increments
Influence of Occupancy
Heterogeneity

13. Occupancy
Simulation Results
100 random
occupant reassignment trials
Current occupant assignment
as benchmark
Heating/Cooling energy reduction
and conditioning miss
Occupancy based control
(15 minutes and 78F)
Occupancy Heterogeneity and HVAC Energy Efficiency
• The relative locations represented the possibilities of influences of occupancy heterogeneity
• Occupancy heterogeneity has significant influences on the HVAC energy efficiency
(0.2% to 12%)

14. THANK YOU !