Systematic Framework for Selection, Use and Optimisation ...ercpeople.ucd.ie/james-odonnell/wp-content/uploads/sites/3/2014/07/... · 2.2 Types of Building Energy Calculation Tools

Department of Civil and Environmental Engineering University College Cork (UCC)

Ireland

Systematic Framework for Selection, Use and Optimisation of Building Energy Simulation Software for a Hybrid Mechanical System

By

Ross J. McCarthy, BEng (Building Services) M.I.E.I

Supervisor: Dr. Marcus Keane A Minor Research Thesis submitted to National University of Ireland,

Cork in candidature for the degree of Master of Sustainable Energy

Systematic Framework for Selection, Use and Optimisation of Building Energy Simulation for a Hybrid Mechanical System

MEngSc Minor Thesis CE5002 Ross McCarthy 2

September 2007

Table of Contents

1 Introduction ................................................................................................................... 4

1.1 General ................................................................................................................... 4 1.2 Research Objective ................................................................................................. 5 1.3 Project Scope ................................................................................................................ 6 1.4 Buildings Current energy use ....................................................................................... 6

2 Simulation Software Selection ...................................................................................... 7

2.1 Simulation Approach ................................................................................................... 7 2.2 Types of Building Energy Calculation Tools ............................................................... 8

2.2.1 Types of Inverse Models ..................................................................................... 10 2.3 Review of Building Energy Software Tools .............................................................. 11 2.4 Mechanical Systems Modelling Requirements .......................................................... 12

2.4.1 General Modelling features ................................................................................. 12 2.4.2. Zone loads .......................................................................................................... 12 2.4.3. Renewable Energy Systems ability .................................................................... 13 2.4.4. Electrical Systems and Equipment ..................................................................... 13 2.4.5. HVAC Systems modelling capabilities .............................................................. 13 2.4.6. Available HVAC equipment components .......................................................... 13

2.5 Initial Software Selection ........................................................................................... 14 2.6 ESP- r ......................................................................................................................... 14

2.6.1 Modelling Approach ........................................................................................... 15 2.6.2 Controls ............................................................................................................... 15

2.7 Matlab/Simulink ......................................................................................................... 16 2.7.1 Past Studies ......................................................................................................... 17

2.8 TRNSYS (Transient System Simulation Program) .................................................... 19 2.8.1 Modelling Approach ........................................................................................... 19 2.8.2 Controls ............................................................................................................... 20

2.9 EnergyPlus ................................................................................................................. 21 2.9.1 Modelling Approach ........................................................................................... 21 2.9.2 Controls ............................................................................................................... 22

2.10 IDA Indoor Climate and Energy .............................................................................. 23 2.10.2 Controls ............................................................................................................. 23

2.11 Selected Software ..................................................................................................... 24 3 Model Implementation ................................................................................................ 25

3.1 Main Components ...................................................................................................... 26 3.1.1 Flat Plate Solar Panels ......................................................................................... 26 3.1.2 Evacuated Tube Solar Collectors ........................................................................ 27 3.1.2 Pumps General .................................................................................................... 27 3.1.3 Plate Heat Exchangers ......................................................................................... 28 3.1.4 Calorifiers ............................................................................................................ 29 3.1.5 Heat Pump ........................................................................................................... 29 3.1.6 Boiler ................................................................................................................... 30

3.2 Dynamic Control and Operation Algorithm .............................................................. 31 3.2.1 Differential Controller On/Off ............................................................................ 31



3.2.2 Three-Way Diverting Valve ................................................................................ 31 3.3 Building Geometry ..................................................................................................... 32

3.3.1 Building Layout .................................................................................................. 32 3.3.2 Fabric ................................................................................................................... 32

3.4 Thermal Analysis ....................................................................................................... 32 3.5 BMS Data Required ................................................................................................... 33

3.5.1 Solar Radiance .................................................................................................... 34 3.5.2 Under-Floor Heating ........................................................................................... 35 3.5.3 AHU Heating Coils ............................................................................................. 36 3.5.4 Hot Water Demand ............................................................................................. 37 3.5.5 Input Data Integrity ............................................................................................. 37 3.5.6 Calibration Data .................................................................................................. 38

4 Model Outputs & Post Processing .............................................................................. 39

4.1 Initial Run ................................................................................................................... 39 4.2 Data and Component Modifications .......................................................................... 41 4.3 Final Outputs .............................................................................................................. 42 4.4 Model Calibration ...................................................................................................... 49

5 Optimisation Methodology ......................................................................................... 51

5.1 Central Plant Optimisation ......................................................................................... 51 5.2 Proposed Model Optimisation method ....................................................................... 52

5.2.1 GenOpt ................................................................................................................ 52 5.2.2 Optimization Algorithms .................................................................................... 53 5.2.3 Data Exchange Method ....................................................................................... 53 5.2.4 TRNOPT (TRNSYS) .......................................................................................... 54

6 Conclusion ................................................................................................................ 55 Acknowledgements .............................................................................................................. 55 References ............................................................................................................................ 58 Appendix A: Future Work ................................................................................................... 60 Appendix B Reviewed Software Capabilities ...................................................................... 64 Appendix C: Previous ERI Simulation work ....................................................................... 64 Appendix D: Calibration Procedures ................................................................................... 65 Appendix E: Co-Simulation Methods .................................................................................. 67 Appendix F: BMS Issues ..................................................................................................... 70 AppendixG: Simulation File ................................................................................................ 70



1 Introduction

The primary goal of this project is to develop a computer simulation model of the

mechanical systems of a low energy building, which can accurately emulate the systems

real life operation, this will provide a launch platform for evaluating energy efficiency

solutions. This project will be based upon National University of Ireland’s Environmental

Research Institute (ERI) located adjacent to the Lee River. The ERI is a low energy

research facility comprising passive solar architecture, high levels of insulation, reduced

infiltration, high thermal mass and ventilation. It will therefore provide an ideal subject for

researching energy efficient solutions.

1.1 General Buildings are responsible for at least 40% of energy used in most countries. With the

construction boom in Ireland in decline, countries such as China and India are dwarfing the

scale of our own experience. It is imperative that we act now to reduce energy use in new

builds and retrofit our current building stock. The built environment can make a major

contribution to tackling climate change and energy use.

The European Commission considers the biggest energy savings are to be made in the

following sectors:

• Residential, with savings potentials estimated at 27%.

• Commercial buildings (tertiary), with savings potentials estimated at 30%.

• Manufacturing, industry with the potential for a 25% reduction,

• Transport, with the potential for a 26% reduction in energy consumption.

These sectoral reductions of energy consumption correspond to overall savings estimated

at 390 million tonnes of oil equivalent (Mtoe) each year or 100 billion per year up to 2020.

They would also help reduce CO2 emissions by 780 million tonnes per year (EU Directive

2006/32/EC).

Ireland has introduced new Part L Building Regulation which seeks a reduction of 40% in

new buildings energy use with mandatory use of renewables. These new regulations have

been welcomed are due to be implemented in 2008. Our current building stock therefore

requires more incentives and awareness in order to make them more energy efficient.



1.2 Research Objective The objective of this research project is to develop a calibrated simulation model which

can emulate the buildings hybrid central plant operation. The model will incorporate

renewable energy systems such as solar water heaters and a water source geothermal heat

pump. This simulation model will therefore have the ability to act as a virtual version of

the physical system. If successfully completed the possibilities for energy efficient

measures can be evaluated, optimised and also play a critical role in predictive controller

methods.

Central PlantModel

SystemOptimisation

Model PredictControllerWhole Building

Simulation

NeuralNetwork

Controller

Co-SimulationIES <VE>EnergyPlus

Future StudiesPV Solar Panels

Small Scale Wind Turbine

Wireless Sensors

Figure 1.1 Project objectives and possible uses.

The above diagram fig 1.1 outlines the focus of this work which is highlighted. Additional

possibilities requiring the completion of this model are shown in grey scale. All of which

are dependent on this launching platform to realise their full potential.



1.3 Project Scope This document can be summarised as follows;

Simulation Software Selection

• Required capabilities.

• Mathematical approaches.

• Initial selection of twenty leading programs.

• Detailed Review of most capable program.

• Selected simulation program.

Model Implementation

• Compiling the model and choosing components

• Controllers required

• Model inputs required from the BMS

Model Outputs

• Initial Outputs

• Adjustments made to model

• Final outputs

• Model Calibration and methodology

Model and Central Plant Optimisation methodology

• Parameters identified for energy saving

• A Proposed method

1.4 Buildings Current Energy Use The energy targets of this building were envisaged to be “very good” by the Building

Research establishment Environmental Assessment Method (BREEAM) rating. Outlined

as follows:

• Electrical energy demand of 60 kWh/m²/annum

• Natural Gas demand of 47 kWh/m²/annum

• Total energy demand of 107 kWh/m²/annum

The measured energy demands thus far are as follows (Swift L., 2007)

• Electrical energy demand of 71 kWh/m²/annum

• Natural Gas demand of 83 kWh/m²/annum

• Total energy demand of 154 kWh/m²/annum



2 Simulation Software Selection

The current available building energy simulation tools are investigated to ascertain their

ability to complete the task. The component based plant model will be applied in full scale

for all the subsystems of the central plant, together with the required control and operating

functions in order to emulate the operating characteristics of the mechanical equipment

within the plant-room.

2.1 Simulation Approach In the development of the central plant simulation model, just linking up all the related

components of equipment and pipework will not simulate the plants operation with

differing loads and climatic conditions throughout the simulated period. Additional

operation and control inputs will be necessary, so that the operating quantity and capacity

of the equipment, as well as the corresponding flow conditions at different portions of

pipework, can be effectively converged through the successive iteration processes (Fong

K.F., 2005). HOT WATER METER(OUTPUT)

SOLAR PANELSMETER (INPUT) AHU COIL METER

(OUTPUT)

BOILER HEAT METER(INPUT) +GAS

UNDERFLOOR HEATINGMETER (OUTPUT)

HEATPUMP METER(INPUT) + ELECTRICITY

WEATHER FILE (INPUT)

Figure 2.1 Model Inputs and Outputs (BMS screen shot)

The central plant model will only then truly reflect the physical system with complete

dynamic operation of the main mechanical plant and associated subsystems for the

fluctuating heating demand throughout the simulated period. The model will be driven by

historical BMS loads used as inputs to the model imposing loads on the simulated

mechanical plant.



The inputs and outputs shown in Table 2.1 will be provided from historical loads from the

past year. This approach will allow detailed analysis of the operation and provide metrics

from which the model can be calibrated. Visual clarity is recognised with the layout of the

model replicating the BMS interface.

Table 2.1 Data Files Required from BMS

Input Data Files Calibration Data Files

Solar Radiation (W/m²) Solar Panel Heatmeter (kWh)

AHU Coils (kWh) Boiler Energy Input (KWh

UnderFloor Heating (kWh) Heat Pump Energy Input (kWh)

Domestic Hot Water (m³/s)

Cooling Coil Circuit (kWh)

2.2 Types of Building Energy Calculation Tools Various documents have categorised the current crop of simulation tools using differing

approaches, the most recent and comprehensive being an ASHRAE Research Project 1051-

RP which states that “Building energy calculation tools can be classified into five generic

groups as applicable to commercial buildings” (Maor I., Reddy T. A., 2006).

(a) Simplified Spreadsheet Programs

These typically assume single zone, steady-state load calculations, equipment inputs (such

as lights, fans, chillers, etc). Many companies develop their own in house spreadsheets

which they can tailor to suit specific requirements. At present Sustainable Energy Ireland

(SEI) is developing a Dwellings Energy Assessment Procedure (DEAP) spreadsheet for

assessing the energy performance of new dwellings for compliance with Part L

(Conservation of Fuel and Energy) of the National Building Regulations.

(b) Modified bin or Simplified system Simulation Methods

Also known as complex spreadsheets these programs allow for multizones to be handled.

They typically allow time-related loads to be scheduled by the hour for different day types

such as weekdays, weekends, holidays, and type of functional energy use (cooling, heating

and electricity use of various secondary and primary equipment). Further, control changes,

such as thermostat setting and deadband, economiser, etc… can be captured. Both energy

use and peak loads on a monthly basis can be determined using these tools. (Maor I.,

Reddy T. A., 2006)



(c) Fixed schematic hourly Simulation Programs

Such as Blast (BSL 1999) and DOE-2 which are commonly used general purpose

simulation tools. These programs are capable of simulating loads, systems, plant and

economics. Although the building loads-modelling module captures transient behaviour,

the algorithms for both secondary and primary systems are steady state (Maor I., Reddy T.

A., 2006).

(d) Modular Variable Time-step Simulation Programs

These programs include TRNSYS (Klein et al. 2004) and ESP-r (ESRU 2005, Clarke

2001) which are capable of determining complete transient and dynamic interaction of

buildings. All thermal based mechanical and fluid flow elements associated with comfort

and energy to be simulated under specified control action using built-in subroutines for

psychrometric and other fluid property determination (Maor I., Reddy T. A., 2006). Both of

these programs are discussed in more detail later in this chapter.

EnergyPlus (Crawley et al. 2004) is currently being developed as a modular variable time

step program based on the strongest attributes of both DOE-2 and BLAST. Previous efforts

to develop a whole building simulation model of UCC Environmental Research Institute

(Morrissey E., IRUSE 2006) have shown this program is limited in its current development

stage.

(e) Specialised Simulation Programs

Specialised programs have been developed through the years to solve specific tasks such as

moisture control, air movement, stratification, plant systems. Many use powerful

mathematical programs, such as Matlab/Simulink with tool boxes such as SimBad

(Riederer 2005).

Programs capable of emulating and assessing control strategies include the modular

variable time step-simulation programs previously mentioned but commercial tools such as

UniSim (Honeywell, 2007) offer a commercially available solution compared to high end

design or research tools.



2.2.1 Types of Inverse Models With recent focus and developments in renewable energy solutions for the built

environment, the need for more capable tools has become evident. This is particularly true

for retro-fit and post construction optimisation where commercial design tools have been

found to be imprecise and inadequate. The subsequent interest in this area has led to the

development of more specialised inverse (or data driven) modelling and analysis methods which

use monitored energy data from the building along with other variables such as climatic variables

(Maor I., Reddy T. A., 2006).

Though a consensus is still lacking in the building professional community, inverse

methods for energy use estimation in buildings and related equipment can be classified into

three broad approaches (Reddy T.A., Anderson, 2002). These approaches are described

briefly as follows:

(a) Black-Box Model

Energy data/metrics obtained from building along with weather dependent and weather

independent loads (internal loads/gains, occupancy, external climate, etc) can be used to

create simple or multi-variable statistical model. A program such as Matlab can also be

used to formulate basic engineering equations or purely database driven models to describe

an operation. The physical meaning of this approach in terms of emulating a system is

crude and restricted for purposes of retro-fit analysis and post-construction optimisation.

(b) Grey-box or Parameter Estimation Model

This approach is similar to a black box model, however more detailed mechanistic models

are used for the HVAC equipment which allows are greater flexibility. Again a program

such a Matlab/Simulink is an example with various toolboxes available at commercial and

research levels.

(c) Calibrated Simulation

This approach uses an existing building simulation program and “tunes” or calibrates the

various physical inputs to the program so that observed energy use matches closely with

that predicted by the simulation program (Stein, 1997). This approach can salvage pre-

construction design simulation and apply them for optimisation/retro-fit analysis.

Calibration techniques are discussed in more detail in Chapter 4. Furthermore, simulation

programs which lack the required capabilities can be coupled with programs such as

TRNSYS, this approach is discussed in chapter 5 which outlines methods for co-simulation



using either an IES or EnergyPlus models of the Environmental Research Institute. This

approach provides a platform for more reliable and insightful prediction compared with

statistical models.

2.3 Review of Building Energy Software Tools In this section, an assessment of the current capabilities of building energy performance

simulation programs applied to modelling the central plant of the ERI building is

conducted. The Building Energy Software Tools Directory lists over 290 programmes for

buildings energy efficiency evaluation (DOE, 2005). A comparison of the features and

capabilities of twenty leading programs is shown below in Table 2.3. The list includes

both fixed schematic hourly simulation programs and modular variable time-step

simulation programs. Table 2.3 Leading Building Energy Simulation Programs

Ener-Win eQUEST IES<VE>

Energy Express ESP-r HAP

Energy-10 IDA ICE HEED

EnergyPlus TRNSYS SUNREL

PowerDomuus TAS TRACE

BLAST BSim DeST

ECOTECT DOE-2.1E

The Comparison study has been published by (Kummert et al. 2005) and has been

modified to provide an initial selection of these programs to be compared in more detail

and to include MatLab/Simulink. A total of 7 tables (Appendix B) have been used to

evaluate the above programs in each of the following areas: General Modelling Features,

Zone Loads, Renewable Energy Systems, Electrical Systems and Equipment and HVAC

Systems.



2.4 Mechanical Systems Modelling Requirements It is important to note that this method was used to select initial programs which would

later require further investigation. Partially implemented features were not included in this

assessment. The assessed tables (Appendix B) are based on vendor-supplied information

and were generated by collaboration of experts and published in 2005. It represents the

most recent and comprehensive study of building energy simulation tools. Appendix A

Reference: Crawley D, Hand J, Kummert M, Griffth B, 2005 “Contrasting the Capabilities

of Building Energy Performance Simulation Programs”.

2.4.1 General Modelling features This table provides an overview of how the various tools approach the solution of the

buildings and systems described in a programmers model, the frequency of the solution,

the geometric elements which zones can be composed and exchange supported with other

CAD and simulation tools. The table indicates that the majority of tools support the

simultaneous solution of building and environmental systems. A few tools support

exchange data with other simulation tools so that second numerical opinions can be

acquired without having to re-enter all model details.

2.4.2. Zone loads This table provides an overview of tool support for solving the thermophysical state of

rooms: whether there is a heat balance underlying the calculations, how conduction and

convection within rooms are solved, and the extent to which thermal comfort can be

assessed. The majority of vendors claim a heat balance approach although few of these

report energy balance (see Table 12). This table indicates considerable variation in

facilities on offer. Eight tools claim no support for thermal comfort. Of those that do,

Fanger's PMV and PPD indices and mean radiant temperature are used by a majority of

tools that calculate comfort. The table indicates that there is a sub-set of tools supporting

additional inside surface convection options. Most tools claim support for internal thermal

mass, but the specifics are not yet included in the table. Users would need to check the

vendors’ web sites for further detail.



2.4.3. Renewable Energy Systems ability This brief table provides an overview of renewable energy systems. The table is based on

entries suggested by vendors and is not yet complete in terms of its coverage of such

designs, what is required of the programmer is to define such designs or the performance

indicators that are reported. With a few exceptions, vendors have few facilities for such

assessments.

2.4.4. Electrical Systems and Equipment This table provides an overview of how electrical systems and equipment are treated in

each tool. The majority of tools claim support for building power loads. Yet most of these

loads are simply scheduled inputs. As with renewable energy systems, support for

electrical engineers is sparse.

2.4.5. HVAC Systems modelling capabilities This table provides an overview of HVAC systems, with additional sections for demand-

controlled ventilation, CO2 control and sizing. Readers should also consult Table 8, which

has sections for other environmental control systems and components. At the beginning of

the table is an indicator as to whether HVAC systems are composed from discrete

components (implying that the programmer has some freedom in the design of the system)

or that there are system templates provided. It is notable that some tools now claim to

support CO2 sensor control of HVAC systems. Automatic sizing is offered by quite a few

tools. Most tools offer a range of zonal and room air distribution devices.

2.4.6. Available HVAC equipment components This table provides an overview of the HVAC components as well as components used in

central plant modelling as well as components associated with domestic hot water. This

table is diverse because it reflects the diversity that vendors currently support. Such

diversity requires the programmer to scan the full table for items of interest.



2.5 Initial Software Selection In the initial software selection no distinction has been made between single simulation

tools or modular programs utilising a suite of tools, however a modular approach is

preferable as it will allow the specific simulation needs to be adopted. It was determined

that both ESP-r and TRNSYS were equally matched whilst EnergyPlus and IDA-ICE also

favoured highly and therefore warrant further analyses. In the next section, each will be

discussed in more detail along with MatLab/Simulink.

2.6 ESP- rESP-r (ESRU 2005, Clarke 2001) version 10.1 is a general purpose, multi-domain-building

thermal, interzone airflow, intra-zone air movement, HVAC systems and electrical flow-

simulation environment which has been under development for more than 25years.

It follows the pattern of ‘simulation follows description’ where additional technical domain

solvers are invoked as the building and system description evolves. Programmers control

the complexity of the geometric, environmental control and operations to match the

requirements of particular projects. Recent examples include Navan town Credit Union

building which required a tool capabilities for modelling complex systems.

Figure 2.6.1 ESP-r screenshot showing interface



2.6.1 Modelling Approach It uses a simultaneous modular integration approach (Trcka et al 2006). System parts are

represented by deriving a set of time averaged heat and mass balance equations. The

system matrix is then solved for each time step obtaining the simultaneous solution for the

overall system. However, the mass balance matrix equations are solved sequentially,

applying iterations if the difference between assumed and final values of the marked

variables exceeds specified tolerance (Hensen et al 2006).

It works with third party tools such as Radiance to support higher resolution assessments as

well as interacting with supply and demand matching tools (Crawley et al 2005). An

extensive publications list exists along with example models, cross-referenced source code,

tutorials. It can also run on any platform and operating system.

2.6.2 Controls System control is explicitly modelled, defining;

o Sensor location and sensed variable,

o Actuator location and actuated variable

o Control low with its settings

Control variable(s) for each component is predetermined by the model algorithm itself. It

does not always have a real world complement. Different sensor/actuator/control low

combinations are possible. There are various (although limited number of) controllers in

the software database.



2.7 Matlab/Simulink Matlab is a powerful mathematical programming tool along with its simulation toolbox

Simulink it has increased usage in many fields. Although Matlab/Simulink roots come

more from a controls backround rather than for mechanical application, it has evolved

through Simulink toolboxes to be applied to many problems. In the field of building and

HVAC, the number of users has increased rapidly in the last few years (Riederer 2005).

Figure 2.7.1 MATLAB/Simulink Screen Shot

The tool is suitable for many applications such as;

o Energy consumption

o Control strategies

o Hydraulic and Air flow studies

o Indoor Air Quality

o Comfort

o Sizing problems

There are a large number of toolboxes available within the package itself such as

Stateflow, Real Time Workshop and Femlab. The latter is a toolbox based on the Matlab

for the modelling and simulation of 2D or 3D heat transfer problems. Dedicated Toolboxes

for simulating Buildings and/or HVAC systems include Carnot toolbox, International

Building Physics Toolbox and Simbad Toolbox to name a few. The latter, Simbad is a

commercial product which can perform transient simulations of HVAC plants with short

time steps. SIMBAD Building and HVAC Toolbox is the first truly dedicated toolbox for

the Matlab/Simulink environment. The toolbox provides a large number of ready to use

HVAC models and related utilities to perform dynamic simulation of HVAC plants. This



toolbox, in connection with other existing toolboxes (neural network, fuzzy logic, and

optimisation) offers a powerful and efficient tool for design and test of controllers.

By combining several toolboxes it is possible to model most systems. The large number of

available toolboxes does not come without problems. Most notable is the fragmented

nature of inputs and outputs defined by different developers and their subsequent

interoperability. Currently there is no convention to define the start and finish of modules

to allow ease of integration.

2.7.1 Past Studies o Multizone building models have been developed by different authors. These are

based either on heat and moisture (de Wit et al. 2004) or on heat (EI Khoury et

al. 2004), the latter providing a graphical user interface for the building

description.

o Specific work has been carried out on conduction in walls (Deque et al. 2001)

and their mathematical reduction (Palomo et al 1997).

o Room modelling has been described either on the perfectly mixed air (Riederer

et al. 2000) or on air distribution in rooms. These can be based on the zonal

modelling approach (Ghiaus 2003, Peng 1996, Peng et al. 1997 or Riederer et

al 2001) or on CFD modelling (Sormunen 2004 and Schijindel et al. 2003)

o Ventilation, airflow and air quality has been achieved by integrating multizone

air flow modelling in the Simulink environment. This work includes the

modelling of moisture phenomena and pollutant transport.

o Hydronic networks have been modelled and simulated (Hegetschweiler 2004)

and (Riederer P. 2003) in order to study water networks and the control of

variable speed pumps in heating or cooling systems.

o Active or smart facades have been modelled including their control algorithms

(Park et al. 2003) and (Stec et al. 2003)

o General HVAC components such as coils have been implemented (Yu et al

2003) and (Husaunndee et al. 1998)

o Heat generation by solar, CHP or Heatpumps;

o Models combining solar-hydrogen-biogas-fuel cell systems, boilers, CHP and

heatpumps models have been implemented into simulink environment.

o Renewable systems; Desiccant cooling systems (Ginestet et al. 2003), ground

coupled heat exchangers have been integrated (Jain, 2003) and (Kumara, 2003)



The fact that Matlab/Simulink is a multidiscipline tool owes itself to its success. The

relative ease of coupling modules from various fields means that it will continue to grow.

Its ability to import or export with other software will ensure its growth and will likely be

an important feature in newly developed or existing simulation software.



2.8 TRNSYS (Transient System Simulation Program) TRNSYS (Klein et al. 2004) is a transient system simulation program tracing its roots to a

joint project between the University of Wisconsin –Madison’s Solar Energy Laboratory

and the University of Colorado.

Figure 2.8.1 TRNSYS Graphical User Interface

2.8.1 Modelling Approach It is based on a modular structure and is capable of solving complex energy system

problems which is described by a FORTRAN subroutine. This is achieved by breaking

down a problem into smaller components which are then solved with iterations for

simultaneous solutions (independent modular approach). The components are simulated

sequentially while the balance of the results is obtained by iteration procedure.

The components are configured and assembled using a fully integrated visual interface

known as the TRNSYS Simulation Studio, while building input data is entered through a

dedicated visual interface (TRNBuild). The simulation engine then solves the system of

algebraic and differential equations that represent the whole energy system. In building

simulations, all HVAC-system components are solved simultaneously with the building

envelope thermal balance and the air network at each time step. In addition to a detailed

multizone building model, the TRNSYS library includes components for solar thermal and



photovoltaic systems, low energy buildings and HVAC systems, renewable energy

systems, cogeneration, fuel cells, etc.

The modular nature of TRNSYS facilitates the addition of new mathematical models to the

program. New components can be developed in any programming language and modules

implemented using other software (e.g. Matlab/Simulink, Excel/VBA, and EES) can also

be directly embedded in a simulation. TRNSYS can generate redistributable applications

that allow non-expert users to run simulations and parametric studies.

2.8.2 Controls The controls applicable to each problem are handled in an explicit manner, as in ESP-r.

However a greater number of controllers are available in its database.

System control is explicitly modelled, defining;

o Sensor location and sensed variable,

o Actuator location and actuated variable

o Control low with its settings

Control variable(s) for each component is predetermined by the model algorithm itself. It

does not always have a real world complement. Different sensor/actuator/control low

combinations are possible. There are various (although limited number of) controllers in

the software database.



2.9 EnergyPlus EnergyPlus (Crawley et al. 2004) was used in a previous attempt to generate a whole

building model of the ERI and failed primarily due to lack of flexibility in the mass energy

balance approach employed, it’s dedicated mechanical capabilities warrants a second look

though. The initial review of this program suggests a relatively strong capability from

vendor supplied information.

Figure 2.9.1 EnergyPlus Data Entry Interface Screen Shot

2.9.1 Modelling Approach It has an independent modular integration approach modular approach (Trcka et al 2006).

EnergyPlus system representation is based on fluid loops, all system components are

attached to them. Most of the components are modelled in steady state fashion.

The loops define the movement of mass and energy within the system and a difference is

made between the air loop, plant loop and condenser loop. The loops are indirectly

connected and each has two logical simulation blocks, supply and demand side. The

demand side places a load to the supply side are both are simulated independently, while

the convergence between their interaction points is checked and if necessary the iteration is

required to ensure that the results among the loops are balanced.



2.9.2 Controls The control modelling is less explicit than that of ESP-r, TRNSYS or MatLab/Simulink.

The representation of control is somewhat artificial, since it uses the knowledge of the

zone load (ERI model required systems to span more than one zone) which is only

available in the simulation model, but not in reality (EnergyPlus 2005). The control is

modelled using a two level system of controllers and set point managers. The controller

can not span a loop manager boundary, meaning that the sensed node and the controlled

device must be in the same loop. The set point manager is able to cross the loop manager

boundary, but this does not represent the building accurately, especially mixed use

buildings.

The user specified time step is currently limited to 10 minutes to ensure the stability of the

results, an adaptive time step that is calculated during run-time, is used for system

simulation and zone temperature updates.

See Appendix C.



2.10 IDA Indoor Climate and Energy IDA ICE (Indoor Climate and Energy) is presented via three different user interface levels:

Wizard, Standard and Advanced (EQUA, 2002). The Wizard level is intended for less

experienced users with particular focus on a certain type of study. The Standard level

corresponds roughly to the graphical user interface (GUI) of a typical 3D graphical multi-

zone BPS tool, requiring the building designer to formulate a meaningful simulation model

in terms of thermal zoning, etc. In the Advanced level, the user is allowed to directly

interact with the mathematical model in a way that is not offered by traditional tools.

Currently, a single wizard level interface called IDA Room is predominant.

Figure 2.6.1 Graphical User Interface example (IDA ICE)

2.10.2 Controls Most control loops are explicitly modelled using separated Proportional or Proportional

integral controllers. Variables such as massflows, temperatures and pressures are never

regarded as given, but are always computed, often via more or less idealized control loops.

To compute, e.g., a cooling load, some large but finite capacity room unit and associated

sensor and controller are always used. Fast local loop dynamics are often not modelled in

the basic library in the interest of calculation time. However, to study fast timescales in

some part of the system, sensor, actuator and relevant process dynamics may be modelled

for the local loop.



2.11 Selected Software From examining the above simulation software, it is evident that either TRNSYS or ESP-r

would be compared to MATLAB/Simulink (including SIMBAD toolbox). A lack of

published papers concerning ICA ICE compared to the others discouraged its use in this

problem. MATLAB or indeed SIMBAD fares better but again compared to TRNSYS and

ESP-r is relatively new. A demo of SIMBAD could not be downloaded to evaluate ease of

use. Both ESP-r and TRNSYS were downloaded. ESP-r was difficult to get started with

and upon reading its manuals, self learning was difficult plus the observation that most

papers concerning complex problems were done within Strathclyde University itself.

Finally, TRNSYS was chosen for better GUI (graphical user interface), larger user

numbers, and a large library of controls and general ease of use. The programmer can be

confident with technical support from TESS (Thermal Energy Systems Specialists) and a

large development group worldwide.

TRNSYS is one of the listed simulation programs in the recent European standards on

solar thermal systems (ENV-12977-2). The level of detail of TRNSYS ’building model,

known as ‘‘Type 56’’, is compliant with the requirements of ANSI/ASHRAE Standard

140–2001.

The level of detail of Type 56 also meets the general technical requirements of the

European directive on the energy performance of buildings (NN, Directive

2002/91/EC.2002.)



3 Model Implementation

A component-based model of the central plant system was built up using TRNSYS, the

controls and operating functions of the system were emulated in the simulation model. The

system modelled comprises of an entirely wet system with the air handling units heating

coil circuit included. The decision to negate the inclusion of the operation of the ahu`s was

taken as such, the mechanical air system serves the laboratory areas were the function is

critical to the operation of those spaces, therefore opportunities for optimisation are less

evident.

Figure 3.1 Components layout in TRNSYS model. The visual layout of the components in the model reflects that of the BMS interface for

clarity and ease of use.



3.1 Main Components Multiple choices are available when choosing components or types within TRNSYS. The

selection of which is determined by factors such as available data both measured and

vendor supplied, complexity and calibration parameters. The latter of which will be

required to rectify inaccuracies between real and simulated results.

A time step of 30 seconds was chosen as it represents 10% of the dominant step. The

dominant time step is 300 sec, which is the sampling rate of data from the BMS. This will

ensure stability in the model.

3.1.1 Flat Plate Solar Panels This component (Type 1) models the thermal performance of the flat plate collectors using

theory along with Viessmann product information. The collector array consists of 24

panels connected in series. The vacuum tubes are also connected in series with this array.

The model assumes that the efficiency vs. ΔT/l T curve can be modelled as a quadratic

equation. Corrections are applied to the slope, intercept, and curvature parameters to

account for the presence of a heat exchanger, identical collectors in series, and flow rates

other than those at test conditions (Klein et al., 2004, TRNSYS 16).

The effects of off-normal solar incidence are not considered in this model. Table 3.1.1

shows the required parameters and inputs used for this component. A Data Reader for

Generic Data Files (Type 9) was used to input direct solar radiance. Table 3.1.1 Flat plate solar panels data.

Parameters Inputs

Number in series 24 Inlet Temperature 20°C

Collector area 2.5m² Inlet Flowrate 0.05kg/s

Fluid Specific heat capacity 4.19kJ/kg.K Ambient temperature 10°C

Collector fin efficiency factor 0.7 Incident Radiation Type-9

Bottom, edge loss coefficient 0.8W/m².k Ground reflectance 0.2

Absorber plate emittance 0.7 Incident angle 35

Aborptance of absorber plate 0.8

Number of covers 1

Index of refraction of cover 1.526



3.1.2 Evacuated Tube Solar Collectors The thermal efficiency of this component (Type71) is identical to the flat panel solar

panels. The component is modelled using a quadratic efficiency curve and a biaxial

Incidence Angle Modifiers.

Table 3.1.2 Evacuated Tube Solar Collector Data

Parameters Inputs

Number in series 24 Inlet Temperature 20°C

Collector area 2.0m² Inlet Flowrate 0.08kg/s

Fluid Specific heat capacity 4.19kJ/kg.K Ambient temperature

10°C

Collector efficiency mode 1.0 Incident Radiation Ext.

Flowrate at test conditions 3kg/s.m² Ground reflectance

0.2

Intercept efficiency mode 0.7 Incident angle 35

Negative of first order Efficiency coefficient

10 kJ/hr.m².K

Negative of second order Efficiency coefficient

0.03 kJ/hr.m².K

Index of refraction of cover 1.526

3.1.3 Pumps General Constant Speed:

This component models the constant speed pumps that are able to maintain a constant fluid

outlet mass flowrate. The starting and stopping characteristics are not modelled in this type

(Type114). Therefore, as soon as the control signal indicates that the pump should be ON,

the outlet flow of fluid will jump to its rated condition. This is due to the time constants

with which pumps react in the model to control signal changes is shorter than the input

time step from the data reader ie. 30 sec simulation time step versus 300sec BMS data.

Table 3.1.2 Pump Components Data

Parameters Inputs

Rated flow rates Kg/s Inlet Temperature °C

Fluid Specific heat capacity 4.19kJ/kg.K Inlet Flow rate kg/s

Rated Power kW Control signal 0-1

Motor heat loss fraction 0 Overall pump efficiency 0.6

Flowrate at test conditions 3kg/s.m² Motor efficiency 0.9

Variable Speed:

This component (Type 110) models the variable speed pumps in the system and is able to

maintain any mass outlet flow rate between zero and rated value. The mass flow rate of the



pump varies linearly with control signal setting. The control signal is determined using an

Equation editor; values between 0 and 1 are calculated as a function of the maximum

flowrate.

As with the constant speed component starting and stopping characteristics are not

considered and the outlet flow of fluid jumps to the appropriate value between 0 and its

rated (MAX) condition 1.

3.1.4 Plate Heat Exchangers This component (Type 5) is modelled as a zero capacitance sensible heat exchanger and is

configured for counter flow mode. The hot and cold side inlet temperatures and flow rates

are inputs. The effectiveness is calculated for a given fixed value of the overall heat

transfer coefficient.

This component relies on an effectiveness minimum capacitance approach to modelling a

heat exchanger. Under this assumption, the inlet conditions are provided. The model then

determines whether the cold or hot side is the minimum (load and source sides)

capacitance side and calculates effectiveness based upon the specified flow configuration

(Klein et al., 2004, TRNSYS 16). The outputs are then computed.

Table 3.1.4 Plate Heat Exchanger Data

Parameters Inputs

Counter flow mode 2 Hot side inlet temperature °C

Specific heat of hot side fluid

4.19kJ/kg.K Hot side flow rate kg/s

Specific heat of cold side fluid

4.19kJ/kg.K Cold side inlet temperature °C

Cold side flow rate kg/s

Overall heat transfer coeff. of exchanger

0.9



3.1.5 Calorifiers This component (Type 534) models a fluid filled, constant volume calorifier with

immersed heat exchangers. Both cylindrical vessels use coiled tube immersed heat

exchangers and are vertical in orientation. The actual system uses two solar fed storage

vessels for maintenance and space requirements, these can be equated as one vessel within

the model as both mirror each other in operation. Mains water is heated fully or partially in

these vessels before entering the quick recovery vessel which has optional boiler heating.

Table 3.1.5 Calorifier Data

Parameters Inputs

# of tank nodes 5 Inlet temperature for port °C

Number of ports 1 Inlet flow rate for port kg/s

Number of immersed heat exchangers

1 Inlet temperature for HX °C

Inlet flow rate for hx kg/s

3.1.6 Heat Pump This component (Type668) models a single-stage water to water heat pump. The heat

pump conditions a liquid stream by rejecting energy to (cooling mode which is disabled) or

absorbing energy from (heating mode) a second. The model uses vendor supplied data for

the capacity and power draw; however, it was necessary to generate this in light of issues

with the manufacturer. These were based on entering load and source temperatures. When

the user defined control signal indicates that the unit should be ON, it operates at its

capacity level until the control signal values changes.

Table 3.1.6 Heat Pump Data

Parameters Inputs

Liquid source specific heat 4.19kJ/kg.K Liquid source Temperature

20°C

Flow rate of liquid source 9 kg/s Flow rate of liquid Source stream

0.05kg/s

Effectivness-Cmin product kJ/s.K Ambient temperature 10°C

Minimum temperature for direct Liquid heating

°C Heating control function

0-1

Minimum temperature for source Liquid heating

°C



3.1.7 Boiler This component (Type 697) models the operation of the gas fired fluid boiler using data

obtained from vendor, it is currently not possible to obtain validated operating efficiencies

due to BMS issues (Swift,L). The model is configured to meet the user-specified outlet

temperature which is constant with a variable mass flowrate determined from variable

speed pumps. The combustion efficiency (Input 6) is used to calculate the boiler thermal

losses. Overall efficiency is lower than the combustion efficiency due to boiler thermal

losses and any cycling effects. The boiler is assumed to be off if the inlet flow rate is zero,

the input control signal is zero, or if the inlet temperature is greater than or equal to the

desired outlet temperature. If the desired outlet conditions cannot be met due to capacity

limitations, the machine will run at its available capacity and the outlet state calculated.

Table 3.1.7 Boiler Data Parameters Inputs

Rated Capacity 185.5kW Inlet fluid temperature 30°C

Fluid Specific heat capacity 4.19kJ/kg.K Inlet fluid flow rate 3.8kg/s

Input control signal 10°C

Set-point temperature 0-1

Boiler efficiency 0.78

Combustion efficiency 0.85



3.2 Dynamic Control and Operation The implementation of the central plant simulation model involves linking the main

components such as heat exchangers, pumps, boiler etc, however to reflect the real plant

operation under varying loads and climatic conditions further inputs were necessary. The

model therefore required operation and control inputs, so that the operating quantity and

capacity of the equipment, as well as the corresponding flow conditions at different

portions of pipework could be effectively converged through the successive iteration

processes.

3.2.1 Differential Controller On/Off The on/off differential controller generates a control function which can have a value of 1

or 0. The value of the control signal is chosen as a function of the difference between upper

and lower temperatures Th and Tl, compared with two dead band temperature differences

DTI and DTI. The new value of the control function depends on the value of the input

control function at the previous.

Figure 4.2.1 Example of On/Off Differential Controller

3.2.2 Three-Way Diverting Valve This component (Type 11) simulates the operation of a flow diverter with one inlet which

is proportionally split between two possible outlets, depending on the value of g, an input

control function determined by a Proportional, Integral and Derivative (PID) controller.

This component (Type 23) calculates the control signal required to maintain the controlled

at the required set point, in this case the average solar storage vessel temperature.



3.3 Building Geometry This section briefly describes the building, although this research focuses on the central

plant an understanding of the subject will help in understanding the results of this study.

The buildings current energy performance metrics have been researched by Swift L., “Post

occupancy performance evaluation of the ERI using standardised building metrics” 2007.

3.3.1 Building Layout The building is rectangular in shape and consists of 3 floors on a sloped site with a partial

basement incorporated on the ground floor. The south façade utilises a high percentage

area fenestration with minimal shading provided from concrete columns.

The building provides several functions housing open plan and single offices, laboratories

and clean rooms. The layout is repeated over the first and second floor in three zones. The

ground floor differs with laboratories south facing and shading from above also and cold

stores and utilities to the rear.

3.3.2 Fabric The table below highlights the primary elemental U-Values associated with the buildings

fabric. Whilst the current values far exceeded the 2002 building regulations they are now

failing in the current regulations.

Table 3.3.1 shows the Elemental U-Values compared with current and future Part L Regulations Element

Proposed ERI Building Regulations

2002

Part L

Building Regulations

2007 Part L

Ground Floor 0.2 W/m2 K 0.45 W/m2 K 0.25 W/m2K

External Walls 0.3 W/m2 K 0.45 W/m2 K 0.27 W/m2K

Glazing: 1.8 W/m2 K 3.3 W/m2 K 2.2 W/m2K

Vent Doors 0.38 W/m2 K 0.45 W/m2 K N/A

Roof

0.19 W/m2 K 0.25 W/m2 K 0.16 W/m2K

3.4 Thermal Analysis A thermographic survey has been conducted on the building (Swift L., 2007). The findings

show that heat losses are most prominent through windows and mechanical vents which

operate with fume cupboards. The windows showed a high level of heat loss along the top

frame, which is a result of no seal being formed when window is closed. The mechanical

vents again are not sealing when shut nor are they insulated.



3.5 BMS Data Required The simulation model will utilise data obtained from the BMS data archive for a 24hr

period in late November. This data was chosen as it represents a time when the central

plant operated closest to its envisaged design. An ideal scenario of utilising an annual set

of metrics is not possible due to mechanical plant failure and issues with the current BMS

system (see Appendix F).

The weather conditions for the period of simulation (fig 3.5.1) represents an ideal weekday

(Tuesday) from which to base the plants operation. With an average outside temperature of

10ºC and a minimum and maximum of 7.5 ºC and 13 ºC respectively, it is ensured that the

heating system will operate.

Outside temperature (21.Nov.06)

0

2

4

6

8

10

12

14

0 5 10 15 20 25 30

Time 24hrs

Out

side

Air

Tem

pera

ture

Figure 3.5 Outside Air Temperature for period of Simulation

The data obtained from the BMS data archive can be categorised into two groups from

which the basis of the model is formed. These groups are discussed in the following two

sections.



3.5.1 Solar Radiance This section shows the data used to essentially drive the models operation. As discussed in

chapter two the use of inverse or data read models require accurate metrics from which the

simulation model can emulate the physical system.

Both the evacuated and flat plate water heater requires data from which their operation can

be simulated. Fig 3.5.1 shows the total solar radiance measures for the simulation period.

Total Solar Radiance (21.Nov.06)

0

100

200

300

400

500

600

700

800

900

19:12 0:00 4:48 9:36 14:24 19:12 0:00 4:48

Time (24hrs)

Sol

ar R

adia

nce

W/m

2

Figure 3.5.1. Graph showing measured total solar radiance over 24 hr period

The graph shows approximately 110W/m² during periods of complete darkness. The solar

radiation meter is situated directly in front of the solar array and is not subject to light

interference from nearby sources. The meter is not calibrated and is being subjected to

stray voltage from electrical devices to its signal cable. Again refer to (Appendix F).

A solution to this problem was to use a TMY2 (typical metrological year) weather file for

the region from which the required solar data was obtained to simulate the solar array,

although cloud cover can not be assessed using this file it was not necessary for the

research to suffer as a result of omitting this data. An attempt at using the above data is

discussed in the following chapter showing further reasons for omission.



3.5.2 Under-Floor Heating Whilst the solar radiance data is utilised to produce energy, the additional model driving

inputs are the heat pump and Boiler. These components are entirely simulated using vendor

data and in the case of the heat pump a text file was generated to assign the operating

performances at part load. The metrics form these components are later used to calibrate

the model.

The under floor heating energy data is used to impose a load on the system, the above

components are therefore required to satisfy these loads. The data obtained shows the

operating trend of the system with the energy transferred to the slab shown to satisfy the

various room loads in the building. The trend is close to on/off control in its nature due to

solar gain (and other climatic variables) and the modulating passive ventilation (See

Appendix A fig A.1). The units are in kWh with a sampling time step of 300sec intervals.

Figure 3.5.2 UnderFloor heating demand over 24 hr period

UnderFloor Heating (HMUF2 21.Nov.06)

0

100

200

300

400

500

600

700

800

900

0 5 10 15 20 25 30 Time (24hr)

Ther

mal

Ene

rgy

kWh



3.5.3 AHU Heating Coils Both the under floor heating and AHU (Air Handling Unit) coils represent significant loads

imposed on the system. The graph below (fig 3.5.3) shows the least variable of the

imposed loads with a sustained demand over the simulated period. The AHU`s primarily

serve the laboratories located in the north facing rooms of the building. Again the units are

in kWh with a sampling time step of 300sec intervals.

These input files are contained in text files which are subsequently read using Type 9 data

readers linked to a load imposing component Type 682.

Figure 3.5.3 Graph showing AHU heating demand trend 24hr period

Air Handling Unit Heating Coils (HMUC1 21.Nov.06)

0

100

200

300

400

500

600

0 5 10 15 20 25 30 Time (24hrs)

Ther

mal

Ene

rgy

kWh



3.5.4 Hot Water Demand The final imposing input to the model is the demand for hot water. The profile shows a

sudden increase in the morning use and again drops suddenly at seven in the evening time.

The uniformity of this graph (fig 3.5.4) suggests a further analysis is warranted to ascertain

its validity. The base demand of 700 litres every 15 minutes during night-time is

unrealistic, however unlike the errors in solar radiance which were substituted with a

TMY2 this issue cannot be solved utilising synthetic data inputs. It has been found that the

hot water load is significantly higher than the design intent (Swift L., 2007)..

Figure 3.5.4 Graph showing hot water demand over 24hr period

3.5.5 Input Data Integrity As outlined, integrity issues pertaining to BMS have allowed a high level of uncertainty to

be associated with the simulations results once the simulation is run. Should the simulation

return favourable results, a process of validation will be required regardless. For the

simulation to truly emulate the plants operation, a set of manually measured or BMS

validated data represents an ideal scenario for this research.

Hot Water Demand

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

19:12 0:00 4:48 9:36 14:24 19:12 0:00 4:48 Time (24 hrs)

M^3

per

900

sec

900s

ec



3.5.6 Calibration Data

The following graphs (fig 3.5.6 & fig 3.5.7) are not required as inputs to run the

simulation; however they are required at a later stage for purposes of calibration. The

initial calibration of the model will compare the measured boiler and heat-pump energy

outputs with those obtained from the simulation model. The boiler shows peak demands

around 10am and to a lesser extent at 8pm which are attributed to AHU heating demands

and hot water draw, the latter of which introduces a degree of uncertainty.

Boiler Heat Ouput (HMB1 21.Nov.06)

0

200

400

600

800

1000

1200

0 5 10 15 20 25 30

Time (24 hrs)

Ener

gy kW

/h

Figure 3.5.6 Graph showing boiler output over 24hr period

The heat pump profile demonstrates the “pulse” nature of its operation serving a high

thermal mass slab. The control sequence is on/off control with is activated when more than

three rooms require heat input. The graph shows a peak demand at noon, this is not

reflected in the under-floor heating profile (fig 3.5.2). The operation of the heat pump is

terminated at 2pm due to mechanical failure (see Appendix F).

HeatPump Meter (HMUF1 21.Nov.06)

0

50

100

150

200

250

21:36 0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48

Time (14 hrs)

Energ

y kW/

h

Figure 3.5.7 Graph showing heat-pump output over 14 hrs. (see appendix F)



4 Model Outputs & Post Processing

Standard building simulation programs typically produce electrical demand and

consumption data as a program output. When modelling existing plants, if the results do

not match actual monitoring data, the programmer will typically “adjust” inputs and

operating parameters on a trial-and-error basis until the program output matches the

known data. This “fudging” process often results in the manipulation of a large number of

variables which may significantly decrease the credibility of the entire simulation

(Troncoso, 1997).

This chapter will discuss the initial runs, modifications and final outputs.

4.1 Initial Run The initials attempts at running the model required adjustments to the components and

model structures. Convergence issues occurred in model due to the order in which the

model “sees” the components. A solution was determined altering the order in which

components are called.

Figure 4.1 Component Order

When working on a fluid loop that is not converging, the trick is to pick

a starting component and then put the remaining components in the order

they are connected.

So to fix the initial non-convergence, the order was changed to P6 control-P6-27-

VacuumTubeCollectors-FlatPlateCollectors-2621SolarThermalStorage-HE_02-23.

Although other minor errors existed such as, incorrect controllers and 3-way valves



operating in on/off manner all of which were resolved. The initial run produced outputs

which would require post processing.

Figure 4.2 Initial solar water temperatures °C

The above graph fig 4.2 shows the fluid temperatures obtained in both solar water heater

types. It can be seen that only a moderate increase in temperature is achieved.

Figure 4.3 Solar Radiance W/m²

It was reasoned in the previous chapter that this data was not accurate. It is further

demonstrated in these graphs (fig 4.3) that regardless of the data’s integrity, more variables

are required other than total solar radiance. The level of information required to simulate

the solar heaters operation is shown in the following section.



4.2 Data and Component Modifications As stated in the previous chapter the solar data input showed a low level of integrity with

further limitations highlighted due to the large number of required of parameters. Fig 4.4

shows a screen shot of the links required to successfully model the solar heaters.

Figure 4.4 Flat Plate data inputs required

Figure 4.5 Evacuated Tube data inputs required



4.3 Final Outputs AHU Coils: The final simulation results generated incorporate the necessary changes discussed

previously. Fig 4.6 shows the heat transfer rates kJ/hr (left axis) to both the AHU coils and

HE-04. Compared to the data obtained from the BMS the AHU coils profile shows slight

smoothening and follows the trend as expected. The transfer of heat to the heat exchanger

is significant as it demonstrates a period when the heat pump was unable to meet the load

imposed on the under floor heating.

Figure 4.6 Shows the Heat Transfer kJ/hr to the AHU Coils and HE-04 (Heatpump)



Boiler:

The profile shown in fig 4.7 shows both the boiler input and heat transferred to the quick

recovery vessel. The boiler burner is operated with on/off control and will therefore not

vary along the Y-axis (right). The frequency at which the boiler is called is signifgant

which is a result of a high demand from the hot water vessel. During this 24 hr period the

boiler is close to running consistently at full capacity.

Figure 4.7 Shows the Heat Transfer in kJ/hr from the Gas Boiler and to DHW.

The demand for hot water is shown on the left axis and denoted in kJ/hr. The initial peak is

as a result of the simulation starting from zero i.e., no thermal capacitance exists in the

system and therefore represents a cold start scenario



Heat Pump:

The modelling of the heat pump required a performance characteristic to emulate its

operation. This data is usually obtained from manufacturer’s data or by conducting a

performance evaluation. In this case a typical performance was generated and applied to

the heat pump through a text file read by the component. Fig 4.8 shows the load imposed

(left axis) by the under floor heating and the subsequent electrical load required to operate

the heat pump (right axis). Again the control sequence is on/ff, this represents a change in

the original design intent for a variable speed proportionally controlled operation

Figure 4.8 Shows the Heat Transfer kJ/hr to the Under-Floor heating Circuits and Electricity used by

the Heat-Pump kJ/hr The under floor heating profile follows the data acquired from the BMS with no deviation.



Hot Water:

As stated previously the demand for hot water is a significant energy use in the building.

The graph below (fig 4.9) shows the flow rate on the right axis against the heat transferred

into the vessel (same as fig 4.7). Uncertainties exist in this data which has had an effect on

the boiler output, essentially forcing the boiler operate at maximum potential.

Figure 4.9 Shows the Hot Water Demand m³/hr and Heat Transfer to Vessel kJ/hr



Solar Water Heaters:

The modelling of the solar array produced favourable results in terms of operation. The

trends follow closely to the solar data provided. The flat plate solar heater temperature is

shown in blue and the evacuated tube collectors shown in red (fig 4.10). As expected the

temperatures reached in the evacuated tubes exceeds the level reached the flat plate by

10°C.

The panels are operated with on/off control which is shown as a solid portion of the graph,

subsequent figures show this operation in greater detail.

Figure 4.10 Shows the Temperatures ºC in both Solar Water Heater Types



Figure 4.11 Vacuum Tube and Flat Plate Temperatures

Figure 4.12 Vacuum Tube Temperatures



Figure 4.13 Flat Plate Temperatures

Figure 4.14 Solar Buffer Vessel Heat Transfer kJ/hr



4.4 Model Calibration Due to integrity issues with the BMS data, it was not possible to adequately calibrate the

simulation model. Until these issues are resolved and validated it will not be possible to

develop this model as intended or can future research in this area pertaining to the ERI gain

recognition. The shortcomings of the BMS have therefore resulted in a lost opportunity for

this low energy research facility. The section will therefore outline the possible methods of

calibration when quality data can be obtained.

Building simulation tools have been used for testing retrofit/optimisation alternatives

worldwide. But the results provided by software would not be worth if the base case was

not correctly calibrated, i.e., the virtual model of the building under analysis must represent

faithfully the thermal and energy behaviour of that building or mechanical system. To

achieve this objective, one has to compare measured performance data with those values

predicted by the software. (Westphal, F.S, Lamberts, R. 2005).

Calibration levels are outlined in Table 4.1 Table 4.4.1 Proposed calibration levels as a function of input data available and time needed for

calibration (Maor et al., 2006) Calibration Levels

Building Description and performance data available for calibration Time needed for calibration

Energy

Invoices

(1yr)

As-built

drawings

Walk

through

site visit

Detailed

Audit*

Short-term

Monitored

end-use

data

Electrical

interval

data (1yr)

Long-term

monitored

data

Data

gathering

Analysis

(**)

Level 1 X X 30 min 1-2 hrs

Level 2 X X X 2-4 hrs 2-4 hrs

Level 3 X X X X 1-2

days

4-8 hrs

Level 4 X X X X X 2-3

days

1-2

days Level 5 X X X X X X 2-4 wks 2-4

days Level 6 X X X X X X X 4-6 mo. 6-10

days



Figure 4.4.1 Structure of Calibration Methodology (courtesy ASHRAE Research Project 1051-RP)

See Appendix D: Methodology and calculation steps for calibrated simulation as per

ASHRAE 14 Guideline (ASHRAE 14, 2002).



5 Optimisation Methodology

Typically the optimisation of building energy simulation models is achieved through

experienced gained by the programmer, termed heuristic methods.

Heuristic methods make use of expert knowledge or experience gained by professionals

over the years in order to reach near optimal design (Maor et al., 2006). This approach

allows existing building simulation tools to be more effectively and efficiently used since it

reduces the number of competing system configurations to a relatively small set to which

detailed simulation tools can then be applied.

With the advent of more complex and innovative design solutions to building energy

systems, the isolation of individual components for optimisation is restrictive. A

mathematical approach is therefore considered.

5.1 Central Plant Optimisation In the case of the ERI, a number of components can interact to share loads and is

determined through a control strategy which was developed by BDP (Building Design

Partnership, Consultants). A calibrated simulation model can be used optimise this control

strategy, essentially challenging set points and storage vessels with priority designation.

Table 5.1 lists of plant systems (ERI) that have been identified as presenting opportunities for

optimisation. Application Controlled component Output to be optimised Optimum start/stop Heating component Start/stop time

Optimum set-back temperature Heating system Set-back temperature

Boiler/Heat-pump sequencing Boilers Heating system efficiency

Load shedding Heating system Priority for heating

Under-floor heating Heating system Hours of operation

Night operated ground water source heat

pumps

Water pump/compressor Start time

Solar control mode Controller Set points

Aquifer preheat from Solar Controller Set points

The above components can be optimised either for running cost reduction, CO2 emissions

reduction or a compromise of both.



5.2 Proposed Model Optimisation method The TRNSYS model can be optimized using an external program called GENOPT; it is an

optimisation program for the minimisation of a cost or emissions function (electricity/Gas)

that is evaluated by an external simulation program (TRNSYS). It can be used with any

simulation program that reads inputs from and write outputs to text files, developed at the

Lawrence Berkley National Lab (Kummert, 2007).

5.2.1 GenOpt To perform the optimization, GenOpt automatically writes the input files for the TRNSYS

program. The generated input files are based on input template files, which are written for

the simulation program that has been constructed. GenOpt then starts the simulation

program, checks for possible simulation errors, reads the value of the function being

minimized (boiler, heat pump etc.) from the simulation result file and then determines the

new set of input parameters for the next run. The whole process is repeated iteratively until

a minimum of the function is found. During the optimisation, GenOpt's graphical user

interface displays intermediate results online. If the simulation problem has some

underlying constraints, they can be taken into account either by a default implementation

or by modifying the function that has to be minimized (Wetter M., GenOpt).. GenOpt

offers a default scheme for simple constraints on the independent variables (box-

constraints), as well as a formalism that allows adding constraints on the dependent

variables by use of penalty or barrier functions

LOGOUTPUT LOG

GENOPT TRNSYS

INITIALISATION COMMAND CONFIGURATION

SIMULATIONINPUT

TEMPLATE

OUTPUT

PROGRAM CALL

RETREVIAL OF SUMULATION OUTPUT

OPTIMISATION SIMULATION

Figure 5.2.1 TRNSYS optimisation procedure using external coupling



5.2.2 Optimization Algorithms The following optimization algorithms can be implemented (Wetter M., GenOpt):

• Generalised Pattern Search algorithms (the Hooke-Jeeves and the Coordinate

Search algorithm), which can be run using multiple starting points.

• Particle Swarm Optimisation algorithms (for continuous and/or discrete

independent variables), with inertia weight or constriction coefficient and velocity

clamping, and with a modification that constricts the continuous independent

variables to a mesh to reduce computation time.

• A hybrid global optimisation algorithm that uses Particle Swarm Optimisation for

the global optimisation and Hooke-Jeeves for the local optimization.

• Discrete Armijo Gradient algorithm.

• Nelder and Mead's Simplex algorithm.

• Golden Section and Fibonacci algorithms for one-dimensional minimisation.

• The following algorithms can be used for parametric studies:

• Mesh generator to evaluate a function on all points that belong to a mesh with

equidistant or logarithmic spacing between the mesh points.

• Parametric search where only one independent variable is varied at a time.

5.2.3 Data Exchange Method The data exchange between GenOpt and TRNSYS is done with text files only. To do the

optimisation, GenOpt, based on one or more input template files, automatically generates

the new input files for the simulation program. To generate such template files, the user

copies the already-defined simulation input files and replaces the numerical values of the

independent variables to be modified with keywords (Klein et al., 2004). GenOpt then

replaces the keywords with the corresponding numerical values and writes the simulation

input files. This approach makes GenOpt capable of writing text input for any simulation

program. In a configuration file, the user can specify how the simulation program can be

started and where GenOpt can find the current value of the cost function. This makes it

possible to couple any external program to GenOpt without modifying and recompiling

either program. The only requirement of the external program is that it must read its input

from text files and write the cost function/CO2 value plus any possible error messages to a

text file (Kummert M., 2007).



5.2.4 TRNOPT (TRNSYS) Recent developments amongst the TRNSYS developer group have improved the link

between GenOpt and TRNSYS with a dedicated component called TRNOPT which can

call the program seamlessly into the model. Essentially TRNOPT is a dedicated TRNSYS

interface for GENOPT. It was been created by TESS (Thermal Energy Systems

Specialists) in order to streamline the optimisation process and it is distribute as part of the

TRNSYS library. With TRNOPT, the entire process of optimising the results from a

TRNSYS simulation reduces to choosing the TRNSYS input file, choosing the variables

which will be varied to optimise the results, and choosing the simulation result to be

optimised.



6 Conclusion

This research project has put in place framework for building energy simulation tools that

were assessed in their ability to simulate a hybrid complex system. The procedure allowed

for an effective initial selection from twenty leading programs. From this, a final review of

the most capable tools was established, this included the following programs:

ESP-r, EnergyPLUS, IDA ICE, TRNSYS and SimBad.

TRNSYS was determined to offer the most capable ability to complete the task of

simulating the ERI central mechanical plant. Funding was secured for purchase of the

program from the National University of Ireland “Green Building Fund”.

The model was implemented over a period of eight weeks, a detailed knowledge of the

capabilities and possibilities of the program were gained during this time. Obstacles were

resolved timely and effectively with expert advice from TRNSYS developers. The

challenge of emulating the control strategy was overcome using a sequence of “trial and

error” on an array of controllers in the program library. A combination of proportional and

on/off controllers was determined to over the most realistic virtual representation of the

physical system.

It has been acknowledged, that obstacles in developing a complex model such as this

required a different approach. The central plant required the simulation of three main

systems (Boiler, Solar heaters and a Heat-pump). These systems should have in hindsight

been simulated separately to begin with, allowing minimal errors when the systems are

subsequently joined. This approach is effectively a time saving method.

The importance of accurate and calibrated data for this inverse method of simulation is

paramount. Integrity issues with the BMS have introduced uncertainties to the generated

model results, in terms of calibrated simulation work on this building the present time

represents an opportunity lost. The future works emanating from this model is essentially a

waiting game until such time that the BMS issues are resolved and metrics collected.

A procedure for evaluating optimisation potential was outlined in Chapter 6. Once the

model is calibrated this mathematical mode of determining optimum set points for plant

operation can be implemented. The theory of this procedure was discussed; it showed that

this form of optimisation is not limited to plant systems complex or otherwise. It highlights



a method for whole building optimisation for design stage, operational stage and retrofit

evaluation. Methods of whole building simulation are outlined in Appendix A which

includes co-simulation amongst current models of the ERI.

Future works dependent upon this model are outlined in Appendix A, which expands on

the initial diagram fig 1.1 showing the possibilities of attainable upon completion of this

model.

As reported in the introduction the energy use in the built environment represent 40% of

the energy use in the EU (EU Directive 2006/32/EC). The challenges in reducing the

energy used in our current building stock is somewhat overlooked with modern more

energy efficient building becoming mandatory distracting policy makers from updating the

current built environment.



Acknowledgements

I wish to thank the following people for their guidance and help in completing this

research.

• Dr. Marcus Keane, IRUSE (Civil and Environmental Engineering Department)

• James O’Donnell, IRUSE, PhD Student

• David Bradley, Thermal Energy System Specialists (TESS), Madison, Wisconsin,

USA.

• Tim O`Riordan, Thermal Energy System Specialists (TESS), Madison, Wisconsin,

USA.

• Jeff Thornton, Thermal Energy System Specialists (TESS), Madison, Wisconsin,

USA.

I would also like to acknowledge the efforts of the TRNSYS Developers Group.

• The Solar Energy Laboratory at the University of Wisconsin-Madison, USA.

• The Centre Scientifique et technique du Bâtiment in Nice, France.

• Transsolar Energietechnik GmBH in Stuttgart, Germany.



References

Crawley D, Hand J, Kummert M, Griffth B, 2005 Contrasting the Capabilities of Building Energy Performance Simulation Programs Clarke J, 2001. Energy Simulation in Building Design, 2nd Edition Energy Systems Research Unit 2002. The ESP-r System for Building Energy Simulation, User Guide Version 10 Series Fong K.F., Hanby V.I., Chow T.T., 2005, HVAC system optimization for energy management by evolutionary programming, Hong Kong Trcka M, Hensen J, Tschnische Universities Eindhoven, 2005, Model and Tool Requirements for Co-Simulation of Building Performance. Riederer P, Couilland N. 2003 Improving HVAC systems using the SIMBAD HVAC and Building toolbox: form sizing and control to performance assessment, ISPRA conference, Ispra, Italy. Riederer P, 2005 MatLab/Simulink for Building and HVAC Simulation –State of the Art. Riederer P., et al. 2001, Development and quality improvements of HVAC control systems in virtual laboratories. Riederer P., et al. 2000. Building zone modeling adapted to the study of temperature control systems, ASHRAE/CIBSE 2000, Dublin, Ireland. De Wit M.H, 2001, WaVo, A Simulation Model for the Thermal and Hygric Performance of a Building. Deque F., Noel J., Roux J.J. 2001 SISLEY: An open tool for transient state

two dimensional heat transfer. 7th International IBSPA Conference, Rio de Janeiro, Brazil, 2001. Peng X. 1996. Modelling of indoor thermal conditions for comfort control in buildings, PhD thesis, Delft, University of Technology. Peng X., van Passen 1997 Simplified modeling of indoor dynamic temperature distributions, Clima2000, Brussels. Simbad, 2004. SIMBAD Building and HVAC Toolbox, CSTB. Stec W., van Passen D. 2003 Defining the performance of the double skin facade with the use of the simulation model, ISPSA 2003. Park et al. 2003. Daylighting optimization in smart façade systems IPBSA 2003 Yu et al. 2003. Fuzzy neural networks model for building energy diagnosis IPBSA 2003 Yu et al. 2003. Modelling analysis on fin tube heating coil of air handling unit BSERT Husaunndee A., Riederee P., Visier J.C 1998, Coil modelling in the SIMBAD Toolbox-Numerical and experimental validation of the cooling coil model. SSB 1998. Jain D., Tiwari G.N 2003., Modelling and optimal design of ground air collector for heating in controlled environment greenhouse. Kumara R., Kaushik S.C 2005 Performance evaluation of green roof and shading for thermal protection of buildings, Building and Environment.



Klein, et al., TRNSYS- A Transient System Simulation Program User Manual, Wisconsin 2005. Boer C.A, 2005 Distributed simulation in industry, PhD thesis, Rotterdam Ganse A. 2005, Distributed processing, University of Washington, Seattle. Fujimoto R.M 2005 Parallel and distributed simulation- introduction and motivation, Georgia institute of technology, College of Computing. Janak M., 1997, Coupling building energy and lighting simulation, IPBSA Prague. Djunaedy E., Hensen J.L.M., 2003, Towards external coupling of building energy and air flow modeling programs, ASHRAE Transactions Zhai Z., 2004 Developing and integrated building design tool by coupling building energy simulation and computational fluid dynamics program, PhD Thesis, Massachusetts Institute of Technology Hensen J.L.M., 1999, A comparison of coupled and decoupled solutions for temperature and air flow in a building, ASHRAE Transactions. Kelly G.E., May W., Kao J., 1994 Using emulators to evaluate the performance of building energy management systems, ASHRAE Transactions. Haves P., Norford L.K., DeSimone M., 1998, A standard simulation test bed for the evaluation of control algorithims and strategies, ASHRAE Transactions. Clarke J., Cockroft J., Conner S., Hand J., Kelly., Moore R., O`Brien T., Strachan P., 2002, Simulation-assisted control in

building energy management systems, ESRU Strathclyde, Glasgow and Honeywell Control Systems, Motherwell UK. Martin A., Banyard C., 1998 Library of system control strategies, BSRIA application guide. Kummert M., 2007, Using GenOpt with TRNSYS 16 and Type 56, ESRU-University of Strathclyde. Klein et al., 2004, TRNSYS 16 – A transient system simulation program, user manual, solar energy lab, University of Wisconsin. Wetter M. GenOpt 2004, Generic optimization program, user manual, version 2.0.0, Lawrence Berkley National Laboratory. Stein, 1997 Energy System Laboratory of Texas A&M University TESS, 2004. TRNOPT v16, Thermal Energy System Specialists, Madison, WI. Troncoso, R., 1997. “A hybrid monitoring-modeling procedure for analyzing the performance of large central chilling plants”, IBPSA Conference Proceedings, Prague, Czech Republic. Maor I., Reddy T. A., 2006, “Procedures for Reconciling Computer-Calculated Results with Measured Energy Data”, ASHRAE Research Project 1051-RP. Swift L,. 2007 “Post occupancy performance evaluation of the ERI using standardised building metrics.” Sustainable Energy Masters Minor Research Thesis.



Appendix A: Future Work

A1. Simulation Assisted Control At present, detailed building energy simulation programs focused primarily in three areas,

emulators, evaluators and encapsulation

Emulators: emulators replace a building and its HVAC systems and use a computer

program to simulate their response to BEMS commands.

Evaluators: in this role, simulation programs can be used to test the efficacy of possible

control strategies. In this case, a detailed model of the building/HVAC system is

established and various control strategies are evaluated in terms of comfort acceptability

and energy efficiency (Haves P. 1998)

Encapsulation: this mode of operation has been previously dismissed as being beyond the

capabilities of detailed simulation programs but has been implemented on a small scale by

collaborative work of ESRU and Honeywell (Clarke et al., 2002)

Although there are potential difficulties associated with simulation-assisted control,

physical models offer the following benefits over “black-box” models (Strachan P., 2002).

They are able to address cause and effect scenarios,

• They can adapt to the impact of changing building use or operation,

• They potentially offer better control through calculation of interactions and can

identify the factors that result in particular building performance,

• They provide the possibility of comparing options for different control strategies

by testing them on the building.



A1.1 Observed problem The need of forecasting is shown in Fig4.1 showing the temperature profile in ERI room

G24 (it was not possible to obtain figures form heat flow meter). This situation can be

encountered in a passive solar

building with large south-facing

windows. If there is no cooling plant,

overheating can occur during a sunny

afternoon, despite the fact that

heating has been required in the

morning.

If overheating occurs then it is too

late to take a control decision for the

heating plant: the heat stored in the

building structure cannot be removed. Fig A1.1 ERI Open Office G24

A reduction of energy consumption would have certainly been achieved if the temperature

rise had been forecasted, in order to prevent unnecessary heating during the morning hours.

Open office Temperature Profile

19

20

21

22

23

24

25

Time

Time (3Hrs)

Tem

pera

ture

oC



A1.2 Possible Solution-Model Predict

MATLABSIMULINK

COMMONDATABASE

IES/ENERGY

PLUS

PREDICTEDWEATHER DATA

(4HR)

CYLON BMSHISTORICAL

DATA

RESTRICT BMS @BUFFER VESSEL

EFFECTIVNESSRATIO

IDEALISEDENERGYUSE KWh

4HR MEASUREDENERGY USE KWh

Weather data transmitted toprogram via Web Link.

These would ideally forecast4hr temperatures, wind

speed and precipatation.

Building Loads generatedusing simulation software willsupplement the actual data

measured during early life ofthe building. These blockloads may be overwritten.

The ideal energy use wouldbe determined from a setcriteria established using

BREEAM at early stage ofdesign.

The recorded energy use willbe benchmarked against the

idealised. An effectivnessratio will be calculated. Thisratio will be used to prioritise

the block loads in thedatabase.

Hourly block loads arestored in matlab. These

blocks correspond to specificexternal weather. Intially

generated using software butreplaced by measured loads.

A signal will issue acommand of yes or no

overriding the Underfloorheating pumps.

CYLONBMS

MOTORCONTROL

PANEL

Figure A1.2 Flow Chart of the possible simulation environment

The initial investigation of determining what figures can be obtained from MET Eireann

showed it was not possible to gain accurate outlooks when temperatures are required.

Following correspondence with the MET office it was decided that this method would

need a general outlook for the day along with historical weather data from the ERI to be

able to generate useful results.

“We can provide the historical data from April 2006 to date, however we do not have a

service that allows you to download 4 hourly figures from our website, once the

observations are collected each hour, it takes at least a day for the hourly observations to

be entered into the database, quality controlled and then made available to customers”.

It is currently an entire research area in itself (Ensemble Prediction, Lang S. 2007)



A1.3 Possible Solution-A Neural Network Controller (ANN) A neural network controller has previously implemented in a similar solar building for

implementation in a hydronic heating system (Argiriou A., et al 2003). The controller had

forecasting capabilities: it included a meteorological module, forecasting the ambient

temperature and solar irradiance, an indoor temperature predictor module, a supply

temperature predictor module and an optimizing module for the water supply temperature.

All ANN modules were based on the Feed Forward Back Propagation (FFBP) model.

In was concluded from this experiment (two rooms) that the ANN controller leads to 18%

of energy savings over the test period with respect to the conventional one. Simulations

showed that energy savings over the heating season range from 13 to 17%, depending on

the heating schedule used.

Figure A1.3 Implemented ANN controller (Argiriou A., et al 2003)



Appendix B Reviewed Software Capabilities See CD

Appendix C: Previous ERI Simulation work

Problems Encountered: EnergyPlus

Previous efforts of using EnergyPlus to create a dynamic model the ERI building as a

whole have encountered problems. As reported by Elmer Morrisey the airside system

modelling posed the greatest difficultly, primarily due to the mass balance approach

applied to air loops as employed by EnergyPlus. This method is incapable of modelling

rooms or zones that require positive or negative pressure in relation to adjacent rooms.

This is particularly evident in the laboratory areas which require pressure differences for

containing cross contamination; to a lesser extent the toilet areas need a similar approach.

The natural ventilation could not be modelled either as EnergyPlus lacks this ability.

Therefore, at present a working dynamic model of the building as a whole does not exist. It

was therefore concluded that the mechanical and passive air system could not be modelled

with this software at present.

Problems also arose with the waterside modelling as the complex control strategy proved

to complex for EnergyPlus to emulate.

As a mixed use low energy building, the laboratory aspects of the building coupled with

passive elements has proved difficult to simulate. Previous findings have similarly

concluded that it is notoriously difficult to effectively moderate low levels of energy use in

laboratory facilities (Federspiel, Zhang et al. 2002).

Despite the above problems it is the authors opinion the EnergyPlus model could be

salvaged utilising co-simulation i.e., coupling the building thermal load model

(EnergyPlus) with a plant model either MatLab/SimLink (SimBad) or TRNSYS.



Appendix D: Calibration Procedures

Methodology and calculation steps for calibrated simulation as per ASHRAE 14 Guideline (taken from ASHRAE 14, 2002) 1) Produce a calibrated simulation plan. Before a calibrated simulation analysis may begin, several

questions must be answered. Some of these questions include: Which software package

will be applied? Will models be calibrated to monthly or hourly measured data, or both? What

are to be the tolerances for the statistical indices? The answers to these questions are documented in

a simulation plan.

2) Collect data. Data may be collected from the building during the baseline period, the retrofit

period, or both. Data collected during this step includes dimensions and properties of building

surfaces, monthly and hourly whole-building utility data, nameplate data from HVAC and other

building system components, operating schedules, spot-measurements of selected HVAC and

other building system components, and weather data.

3) Input data into simulation software and run model. Over the course of this step the data

collected in the previous step is processed to produce a simulation-input file. Modelers are

advised to take care with zoning, schedules, HVAC systems, model debugging (searching for and

eliminating any malfunctioning or erroneous code), and weather data.

4) Compare simulation model output to measured data. The approach for this comparison varies

depending on the resolution of the measured data. At a minimum, the energy flows projected by

the simulation model are compared to monthly utility bills and spot measurements. At best, the

two data sets are compared on an hourly basis. Both graphical and statistical means may be used

to make this comparison.

5) Refine model until an acceptable calibration is achieved. Typically, the initial comparison

does not yield a match within the desired tolerance. In such a case, the modeler studies the

anomalies between the two data sets and makes logical changes to the model to better match the

measured data. The user should calibrate to both pre- and post-retrofit data wherever possible

and should only calibrate to post-retrofit data alone when both data sets are absolutely

unavailable. While the graphical methods are useful to assist in this process, the ultimate

determination of acceptable calibration will be the statistical method.

6) Produce baseline and post-retrofit models. The baseline model represents the building, as it

would have existed in the absence of the energy conservation measures. The retrofit model

represents the building after the energy conservation measures are installed. How these models

are developed from the calibrated model depends on whether a simulation model was calibrated

to data collected before the conservation measures were installed, after the conservation measures

were installed, or both times. Furthermore, the only differences between the baseline and

postretrofit models must be limited to the measures only. All other factors, including weather and

occupancy must be uniform between the two models unless a specific difference has been



observed that must be accounted for.

7) Estimate savings. Savings are determined by calculating the difference in energy flows and

intensities of the baseline and post-retrofit models using the appropriate weather file.

8) Report on observations and savings. Savings estimates and observations are documented in a

reviewable format. Additionally, sufficient model development and calibration documentation

shall be provided to allow for accurate recreation of the baseline and post-retrofit models by

informed parties, including: input and weather files.



Appendix E: Co-Simulation Methods

E.1 Co-Simulation/Coupling Current building energy simulation tool have to an extent been surpassed by complex

buildings in terms of shape, layout, functionality and services. The requirements for

flexibility and adaptability have increased, as has been experienced by previous attempts of

whole building simulation models in the ERI case. An efficient way forward would be to

share developments and to reuse existing component models. This approach can therefore

best take advantage of advances in various programs to benefit the model as a whole, i.e. to

use the best simulation model available without being limited.

E.1.1 External Coupling

The external coupling approach brakes the boundaries between different simulations and

by that introduces the potential to pool resources. Compared to the traditional approach,

external coupling generates several advantages (Boer C.A., 2005),(Ganse A.,

2005),(Fujimoto., 2005),

o reusability of already existing (legacy) software,

o combination of heterogeneous technologies,

o collaborative model design and development process,

o information hiding,

o scalability and fault tolerance

o geographically distributed components, and

o potentially reducing model execution time & more available memory.

Some software-specific work has been done by others regarding the external coupling in

the field of building performance simulation in general such as;

o ESP-r and Radiance (Janak M., 1997)

o ESP-r and Fluent (Djunaedy E., 2003)

o EnergyPlus and MIT-CFD (Zhai Z., 2004)

In addition to the general to the above there has been several developments regarding co-

simulation in the domain of HVAC systems and control. For example TRNSYS developers

introduced a type 155, defined a MatLab connection. Matlab is launched at every TRNSYS

time step as a separate process. The type 155 communicates with the MatLab engine



through a component object model interface. Any MatLab/Simulink can be run within

TRNSYS (Bradley D. 2005). The latest version of TRNSYS 14.0 has essentially made this

link transparent.

These efforts within the building simulation domain show a great need for interoperable

simulation environments.

E1.2 Coupling Strategy

There are two different coupling strategies (Hensen J.L.M 1999),

• Ping-pong coupling, and

• Onion coupling

With ping-pong coupling, distributed models run in sequence, where each model uses the

known (from the previous time step calculation) output values of the coupled model.

Whilst onion-coupling strategy requires that models iterate within each time step until the

difference between the calculated and estimated values falls within a specified predefined

tolerance.

However, it has been shown that for a sufficiently small coupling time step, both of these

strategies give the same results. There is only one information exchange between the

coupled programs in each direction per simulation time step.

E1.3 Coupling TRNSYS and IES

To allow for such a model to be created in TRNSYS simulations the developers have

created components in the TRNSYS code that explicitly link to the external programs,

execute the models and provide some method of data transfer between the programs.

Currently links have been created for Engineering Equation Solver (EES) (Klein 2004),

MATLAB™ and Simulink™ (Mathworks 2003), and Microsoft Excel™ (Microsoft 2004).

These components work by having TRNSYS call the external program at each iteration

and executing the model in its program. The results are then passed back to TRNSYS

where they can be used in a TRNSYS simulation like any other results from a standard

component.



TRNSYS MATLAB orEXCEL IES <VE>

TYPE 155

Figure E.1 Possible method of Linking TRNSYS and IES

As it is possible to export data from an IES model to Excel which subsequently could also

be exported to Matlab, it provides the greatest chance of co-simulation. Time steps in both

programs can be user defined hence synchronisation would not be an issue. Initially the

programs can be simulated in a two stage process where, firstly IES will generate the

simulated loads applicable to the outputs required by TRNSYS model which can then

simulate the central plant system.



Appendix F: BMS Issues

F.1 Building Energy Management System Issues • No Units on graphs • Incorrect units on Y-axis • Heat meters giving figures and profiles of room temperatures • Datalog interface not functioning • Meters not functioning for most of year • No integrity in 300sec and 900sec data supplied • System crashes when more than 4 days is downloaded • Solar radiance meter measuring sunshine at night





Appendix G: Simulation File

See CD

Documents

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