occupancy and hvac energy
Post on 09-Feb-2017
352 Views
Preview:
TRANSCRIPT
How Does Building Occupancy Influence Energy Efficiency of HVC Systems
Zheng Yang
Zheng Yangwww.zhengyang.me
PhD Candidate Viterbi FellowInnovation in Integrated Informatics Lab (i-LAB.usc.edu)
Department of Civil and Environmental EngineeringUniversity of Southern California
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
HVAC Energy EfficiencyInefficiency: 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 CharacteristicsEffects have decreased (Guerra-Santin 2010)
Governments have introduced regulations and policies
New Technology and SystemsInfeasible and unpredictable (USGBC 2010)
Existing buildings have already installed HVAC systems
Demand driven ControlReact to actual demands (CIBSE 2012)
Based on real space loads to keep desired conditions
Occupants Control PoliciesActual Demands
Difference:
Figure. The importance of occupant in HVAC energy consumption
Occupancy and HVACOccupant activity, control preferences and personal information
Heating/Cooling, Terminal and Setpoint
Terminals Setpoint
Demands Medium
Heating / Cooling
Supply side
Loads
Control unit and interfaceTemperature range (deadband) Thermostat
Primary parameter
Until 2011, 90% of actively conditioned buildings Thermostat Setpoint(ASHRAE 2012, Johnson Control 2012)
03
SETPOINT
HVAC Respons
e
Thermal Environment
Occupant
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;
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 InputEnergy Discrepancy Explanation
Actual Energy Data InputCalibration Evaluation
Default and Autosized
Insignificant Parameters
...
Multi-level building energy model calibration
(Zheng et al. 2014)
Occupancy Transitions
Occupied period – Setpoint; Unoccupied period – Setback;
Setpoint Float Setback Reconditioning
Effective EffectiveIneffective
Occupied Unoccupied Occupied
TimeFigure. Deviation between occupancy and effective loads
Occupied/Unoccupied Transitions ≠ Effective/Ineffective Loads Transitions
A portion of the loads during unoccupied periods = Effective loads
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
35Setp
oint
/Set
back
Sch
edul
e (M
in)
Setpoint/Setback Distance (K)
Energy Reduction (%)
1 2 3 4 5 6 7 8
0
5
10
15
20
25
30
35Setp
oint
/Set
back
Sch
edul
e (M
in)
Setpoint/Setback Distance (K)
Conditioning Miss (%)
Stochastic Occupancy Variations ~ effective heating/cooling loads
Degree of Occupancy Variation
Deterministic
Stochastic
Long-term OccupancyHabitual patterns Represents typical effective loads
Real-time OccupancyOccupancy status for specific time Represents instant effective loads
Occupancy Variations
Euclidean distance between the actual daily occupancy versus the occupancy profile
Deviation of daily real-time occupancyFrom occupancy profileCalculate the Daily average
variation degree
Simulation ResultsDaily energy reduction and
conditioning missOccupancy 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%)
Presence Probability
Time
Presence Probability
Time
Presence Probability
Time
Presence Probability
Time
OccupancyHeterogeneous occupancy
Effective LoadsHeterogeneous load distribution
Occupancy Heterogeneity
Long-term Occupancy(Occupancy Profile)
Load Redistribution
Hierarchical and ConditionalOccupant 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
Occupancy
Simulation Results100 random
occupant reassignment trialsCurrent occupant assignment
as benchmark
Heating/Cooling energy reduction and conditioning missOccupancy 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%)
top related