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%)

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