Coupling Occupancy Information with HVAC Energy Simulation: A Systematic Review of Simulation Tools

Zheng Yang PhD Candidatehttp://www.zhengyang.me

Innovation in Integrated Informatics LabInformatics for Intelligent Built Environment

Civil and Environmental Engineering DepartmentUniversity of Southern California

Simulation Vs Field Experiment

(Siroky 2012, Pisello 2012, Huang 2013)

Feasible all the time

Alternatives before being implemented

Less expensive and time consuming

Reversed after implemented

Control factors that cannot be controlled in a field experiment

Evaluate the sole consequences of one control parameter

Non-intrusion

Output different levels of results

Easier for analysts to interpret results

Advise case-by-case design

Advantages

Virtual representation and reproduction of energy processes

DOE Building Energy Software Tools Directory with 405 programs

• Whole Building Analysis

• Codes and Standards

• Materials, Components, Equipment and Systems

• Other applications

Energy simulation Renewable Energy Retrofit Analysis Sustainability/Green Buildings

Source: http: apps1.eere.energy.gov/buildings/toos_directory/

100+ Programs

Literature Review and Gap Analysis

Simulation ≠ Real Energy Consumption

Research Gaps

NO research - systematically analyze the coupling of

occupancy and HVAC energy simulation

(Yan 2008, Waddell 2010, Henninger 2010, Crawley 2008, Zhu 2012, Andolsun 2008)

• Accuracy and reliable of simulation programs;• Advantages and disadvantages of simulation programs;

ComparisonStudy

• Effects of occupancy on HVAC energy consumption;• HVAC response to occupancy based HVAC controls;

Discrepancies from different programs

First PrincipleThermodynamics

Interior Impacts

Exterior Impacts

Radiation

Convection

Figure. The importance of occupant in HVAC energy consumption

Occupancy and HVAC

+ Conditioning RequirementHeat Gain

Demand-driven HVAC Control

Heat

Balance

HVAC

Modeling

Load

Calculation

Occupancy HVAC

Connection

HVAC

Simulation

Occupancy

Heat GainConditioning

Requirement

Importance of Occupancy

Reduce HVAC Energy

Consumption

Occupant Comfort and

Building Functionality

Effects of Occupancy on

HVACEnergy Consumption

HVAC Response to Occupancy-

based Control Strategies

Simulation

Program

Coupling Occupancy with HVAC Energy Simulation

Figure Occupancy and building HVAC energy simulation

Commonly used Simulation Programs

1

2

Base case and reference buildings

Test bed building in different programs

Lack actual occupancy information

Different input requirements bring additional deviations and uncertainties

Theoretical Comparison

SYSTEMSLOADS PLANTS

Occupancy

BDL

ECONS

Figure. Energy simulation in DOE-2 (arrow shows the flow of information)

Sequential + No Feedback

DOE-2

Figure. eQuest Graphic Interface

Heat transfer and balance Static space temperatureNo strict heat balanceFour heat transfer surfaces

Load Calculation

System component loadsSimplify system issues

Customized weight factors

Occupancy-HVAC connection Sequential loads calculationLimited feedbackLack of loads update

HVAC Modeling

Predefined system typesLimited sources

Strict requirements

HVAC Simulation

LSPE sequenceConstant temperature

Condition at previous time

EnergyPlus

Figure. OpenStudio Graphic Interface

Heat transfer and balance Load Calculation Occupancy-HVAC connection

State space methodStrict heat balance

Predict-correct processFeedback and update

Incorporate with previous timeSurface and air heat balance

Figure. Energy simulation in EnergyPlus (arrow shows the flow of information)

Simultaneous + Update

SYSTEMS

LOADS

Occupancy

Manager

ECONS PLANTS

Customized performance curve

ModularityHVAC ModelingAir loops

Water loops

HVAC Simulation

Figure. IES-VE Graphic Interface

IES-VE

Apache Thermal ModuleModules

ModelITModule

SunCastModule

RadianceIESModule

SimulexModule

IndusProModule

MacroFloModule

VISTAModule

….

Occupancy

ApacheSim ApacheCalc ApacheLoadApacheHVAC

VISTA

MacroFlo

H

Heat transfer and balance

One- dimension conductionUniform thermal conditionStirred tank temperature

L

Load Calculation

Dynamic LoadsAir nodes to space

Heat transfer and balance

O

Occupancy-HVAC connection

Occupancy profileAdmittance techniqueNo variant internal loads

H

HVAC ModelingPre-defined wizards

System prototyping autosizingCustomized components

S

HVAC Simulation

Simultaneous solutionSimulation with airflow analysisApacheSim, ApacheHVAC and Macroflo

TRNSYS

Figure. TRNSYS Graphic Interface

mechanical and electrical system simulationTransient

Component based simulation

DLL (dynamic link library) structure

Co-simulation with other programs

Simulation of individual componentsAcyclic flow or loop

Simultaneous convergence

TRNSYS

H L

O

M S

Heat transfer and balance Load Calculation

Occupancy-HVAC connection

HVAC Modeling HVAC Simulation

Multiple air nodesIterations of components

Utility componentsBuilding components

Customized DLLs

Input-output linkRuntime calls from outside

Standard librariesDevelopment by programming

OccupancyLab Dll

Input

TRNDll

TRNExe

Call Component

Equation Solver

Simulated Output

ESP-r

Figure. ESP-r Graphic Interface

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Physics Modeling

Computational Fluid Dynamics Building System

Simulation

Open Source

+ Research Oriented

Heat transfer and balanceLoad Calculation

Occupancy-HVAC connection

HVAC Modeling HVAC Simulation

Heat gain - thermal networkIntegrated with air fluid dynamicsData exchange with HVAC network

Crank-Nicholson differenceFinite difference nodes

Energy flow control volumeInterconnected nodal networkComponent Interdependency

Equation set for load state

Assembly of componentsStandard librariesNetwork connections

Individual network solverFinite difference method

Convergence of all networks

Occupancy

Airflow

Network

Building Thermal

NetworkHVAC Network

Conceptual energy simulationLinear system – sequential simulation

High computational efficiencyShallow learning curve

Manage simulation parametersThermal-dynamics

Real-time heat balanceAccurate temperature estimate

Simultaneous simulationBack forward feedback and update

Shallow learning curve

Integrated modelEfficient for large and complex system

Less knowledge requirementShallow learning curve

Different modules for loads calculationInaccurate occupancy- associated loads

Fail to specify certain HVAC settings

No assumption or defaultFlexibility and customization

Open Source and component based

Cannot differentiate occupancy impactsSteep learning curve

Requirement for system settings

Research orientedFlexible and holistic

Accurate simulation of network interactions

Lack autosized and default settingFail in complicated and tentative tasks

Steep learning curveKnowledge for thermal dynamics and physics

Multi-level

1Dual Level AccuracyMacro level: Overall energy - a building or a building system;Micro level: Decompose energy consumption - functionality;

Robustness

2RobustnessRobust to the changes resulting from the HVAC being operated differently

Calibration Framework

1. Initial energy modeling;

2. Sensitivity analysis;

3. Parameter estimation;

4. Discrepancy analysis;

5. Discrepancy minimization;

1

2

3

4

5

Five main steps:

High Performance Computing and Communication (HPCC)

GPU-accelerated supercompuaterRanked 5th in the nation

Figure. HPCC Source: USC Website

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