comparisons of building energy simulation softwares
TRANSCRIPT
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
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