# Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq

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Energy 74 (2014) 762e774Contents lists avaiEnergy

journal homepage: www.elsevier .com/locate/energyAssessment regarding energy saving and decoupling for different AHU(air handling unit) and control strategies in the hot-humid climaticregion of Iraq

Raad Z. Homod*

Department of Petroleum and Gas Engineering, University of Basrah, Qarmat Ali Campus, 61004 Basrah, Iraqa r t i c l e i n f o

Article history:Received 31 January 2014Received in revised form9 July 2014Accepted 16 July 2014Available online 12 August 2014

Keywords:Decoupling HVAC systemImproving control performancePMV modelHVAC energy efficiencyOptimal thermal comfort* Tel.: 964 7821731696; fax: 964 60 389212116.E-mail addresses: raadahmood@yahoo.com, raad.h

http://dx.doi.org/10.1016/j.energy.2014.07.0470360-5442/ 2014 Elsevier Ltd. All rights reserved.a b s t r a c t

In a hot and humid climate, HVAC (heating, ventilating and air conditioning) systems go through rigorouscoupling procedures as a result of indoor conditions, which are significantly affected by the outdoorenvironment. Hence, a traditional method for addressing a coupling setback in HVAC systems is to add areheating coil. However, this technique consumes a significant amount of energy. Three different stra-tegies are designed in a hot and humid climate region, such as Basra, for AHUs (air handling unit), andtheir evaluations of decoupling are compared. The first and second strategies use the same feedbackcontrol references (temperature and relative humidity), except the second one also uses a reheating coiland a wet main cooling coil. The AHU (air handling unit) of the third (proposed) strategy is equippedwith a dry main cooling coil and a wet pre-cooling coil to dehumidify fresh air, which allows thecontroller to handle the coupling problem. Furthermore, the proposed strategy utilises the PMV (pre-dicted mean vote) index as a feedback control reference to increase optimisation parameters that providemore flexibility in meeting the thermal comfort sensation. The adaptive control algorithm of nonlinearmultivariable systems is adopted to coordinate these three policies of optimisation. The results of thethree strategies show that the proposed scheme achieved the desired thermal comfort, superior per-formance, adaptation, robustness and implementation without using a reheating coil.

2014 Elsevier Ltd. All rights reserved.1. Introduction

In recent decades, studies on the parameters of HVAC (heating,ventilating and air conditioning) systems, such as the temperature,PMV (predicted mean vote), HVAC system structure volume andcontrol strategies, have demonstrated high performance in HVACsystems, particularly in regard to saving energy [1]. Temperature iscommonly used as the thermal comfort control objective in earlyHVAC systems [2,3]. However, temperature alone does not ensure aperson's thermal comfort [4]. Temperature and relative humidityare coupled; hence, it is difficult to control both factors when eachhas its own strict set point [5]. But, the demands for modern HVACsystems regarding highly systematic products, material integrationand energy integration have resulted in strictly coupled processes.This coupling has exposed many of the uninvited characteristics ofHVAC systems, which are reflected in the limitations of the classicalcontrollers, such as PID (Proportional Integral Derivative), that areomod@uobasrah.edu.iq.used to manipulate the AHU (air handling unit) inputs. Further-more, the currently used PID tuning techniques are inadequatewhen dealing with MIMO (multi-input, multi-output) processes[6,7]. PI (Proportional Integral) and PID controllers are commonlyused in HVAC systems due to their simplicity in structure and theirrelative effectiveness; additionally, the units can be easily under-stood, which makes them practical to implement [8].

Usually, the decouplingmethod is adopted to release or alleviatethe coupling of two ormore of the control objectives in two ormoreof the interlaced loops, which is a difficult task for most of the plantmodel because all of the decoupling techniques have limitations[9,10]. The conventional solution includes adding a reheating coil toaddress this coupling setback. However, the use of a reheating coilincreases the power consumption through the control of the RH(relative humidity) in the conditioned space when the thermalcomfort is maintained at an acceptable level [11,12]. Generally, twotypes of decoupling control systems are currently used: static anddynamic. Static decouplers are effective when high response con-trols are not required to oversee the processes [13]. Additionally,the design of static decouplers is straightforward, and theirimplementation is based on the inverse process of steady state

Delta:1_given namemailto:raadahmood@yahoo.commailto:raad.homod@uobasrah.edu.iqhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.energy.2014.07.047&domain=pdfwww.sciencedirect.com/science/journal/03605442http://www.elsevier.com/locate/energyhttp://dx.doi.org/10.1016/j.energy.2014.07.047http://dx.doi.org/10.1016/j.energy.2014.07.047http://dx.doi.org/10.1016/j.energy.2014.07.047

Nomenclature

SymbolsA surface area, m2

C heat capacitance, J/CdEs/dt rate of change in storage energy of the system, J/sE;in energy rate entering the system, J/sE:out energy rate leaving the system, J/sM mass, kgCp specific heat, J/kgCm: mass flow rate, kg/sMcp heat capacitance, J/CT temperature, Cou humidity ratio, kgw/kgdah latent heat/heat transfer coefficient, J/kg, W/(m2C)Q : cooling load, WCCF surface cooling factor, W/m2

U construction U-factor, W/(m2C)DT cooling design temperature difference, COFt, OFb, OFr opaque-surface cooling factorsDR cooling daily range, CCFfen surface cooling factor, W/m2

UNFRC fenestration U-factor, W/(m2C)PXI peak exterior irradiance, W/m2

SHGC solar heat gain coefficientIAC interior shading attenuation coefficientFFs fenestration solar load factorEt, Eb, EDpeak total, diffuse, and direct irradiance, W/m2

Tx transmission of the exterior attachmentFshd fraction of the fenestration shaded by overhangs or finsL site latitude, NSLF shade line factorDoh depth of the overhang, mXoh vertical distance from the top of the fenestration to the

overhang, mFcl shade fraction closed (0e1)j exposure (surface azimuth), measured as degrees from

southV; volumetric flow rate, L/s

DF infiltration driving force, L/(s cm2)< thermal resistance, C/WNoc number of occupantsNbr number of bedroomsaroof roof solar absorbancet time constant, sI infiltration coefficientDu indooreoutdoor humidity ratio difference, kgw/kgda

Subscriptsm air in mixing boxr room/returno outsideos outside supplyi insideHe heat exchangera airw wateraHe air in the heat exchangerL leakageWin water inputWout water outputWl wallroom inside roomout outside roomg glassfg heat of vaporizationOpq opaqueinf infiltrationfen fenestrationf indoor and outdoort at time tflue flue effectivees exposedul unit leakageig internal gainsl latents sensible/supplyfur furniturecl closed

R.Z. Homod / Energy 74 (2014) 762e774 763gains. However, static decouplersmay not always be able to providesatisfactory control performance. In contrast, dynamic decouplersrequire detailed process models, but they provide better perfor-mance than static decouplers provide [14,15]. For practical opera-tions, the emphasis is typically placed on suitability and causalityneeds, which makes precise configurations difficult to achieve,especially for high-dimensional MIMO processes. To settle thesedifficulties, most of these methodologies focus on TITO (two inputand two output) systems [16,17]. The main shortcoming of thedynamic methods lies in the complexities of the decoupler ele-ments, which are obtained from the apparent process model. Thedifficulty becomes greater for sophisticated plants because thetechnique incorporates the determinant of the model transferfunction [18]. Additionally, the requirement for the decoupler isthat all of its elements must be proper, causal and stable [19]. A fewstudies in the literature have focused on the inverted decouplingmethods that are used to reduce variable interactions in the process[18e22]. Gagnon [10] demonstrated that the performance ofinverted decoupling depends on the scheme of implementation.When inverted, decoupling is implemented with a lead-lag anddelay function process, and the control performance retreats.Normalised decoupling control design methodology was used byShen [23]. For this type of decoupling, the ETF (equivalent transferfunction) of each element in the transfer function matrix wasrequired to derive the closed-loop of the plant model, including thealgorithm of the control system. Then, the decoupler was obtainedby multiplying the inverse of the ETF by a stable, proper and causalideal-diagonal transfer function.

This paper seeks to analyse and discover the paramount choiceof controlled parameters in the HVAC systems, which are reflectedin optimisation controller performances. However, the controller'sperformance is related to buildings' energy efficiency, which ismost directly affected by the decoupling problem. Therefore, in thisstudy, the extensive and elaborate models of a building that hasHVAC system components are used to simulate a real system.Deriving the matrices of decoupling, inverted decoupling or ETFfrom such a complex model is challenging because all of its ele-ments must be proper, causal and stable. In concision, the HVACcontrol systems use both temperature and RH as references insteadof using temperature only, which is what the earlier mode did.Because temperature and RH are coupled, it is difficult to controlthem separately for a certain desired value [11].

R.Z. Homod / Energy 74 (2014) 762e774764It is possible to solve a problem in which the variables of tem-perature and relative humidity are coupled. The first modification inAHUs is the addition of a fresh air pre-cooling coil that is used toalleviate the coupling intensity, which is particularly necessary inhumid climates. The secondmodification for control objectives is theincrease of the optimisation parameters of the output controller byadding amodel of the PMV index in order to evaluate indoor thermalcomfort. Next, decoupling and reduction in energy are simulated bycomparing three different systems under real weather conditionswithin certain set point comfort limits. The first system is a con-ventional system in which the objective is to achieve the tempera-ture and relative humidity that are within the limits of the desiredconditions. The second system is similar to the first, with the onlydifference being the addition of a reheating coil and a wet maincooling coil in AHUs that are used to solve the coupling problem.However, these additional reheating and wet main cooling coilsdouble the energy consumption of the unit due to the addition oftwo processes: an implemented sub-cooling process that reduces theRH and reheating the supplied air in order to meet the desired levelsof thermal comfort. The third system is the same as the first, but ithas an additional pre-cooling coil and controller objective where aPMV model is added to facilitate the controller optimisation for fouroutputs (i.e., the dry bulb temperature, the radiant temperature, therelative air velocity and the relative humidity for an indoor condi-tioned space). Controller (TSKFIS (TakagieSugenoeKang fuzzyinference system)) optimisation is achieved bymanipulating the fiveAHU inputs (control outputs), which are in the form of the flow rateof chilled water for the pre-cooling coil and themain cooling coil, theflow rate of the supply air (fresh air and return air) and the fan speedof the supply air. Additionally, the PMV model strategy does notrequire the use of a reheating coil for decoupling purposes.

The main contribution of this paper is to address the couplingproblem, which arises in the hot and humid climatic region ofSouth Iraq, bymodifying the AHU and applying the algorithm of theadaptive multi-variable control TSKFF (the Takagi-SugenoeKangfuzzy forward).

2. Control system design

The present paper attempts to address the shortfall on energysavings and decoupling for buildings with HVAC control systems inthe hot and humid climatic region of Iraq. Careful assessments insimulated environments are considered. The PMV model is addedto enable controller decoupling of temperature and RH. Increasingmanipulation parameters are used to compensate for any boundedvariations that may arise due to the limitation of the dampersrange. This is considered as a limitation because the HVAC controlsystems have set upper and lower control limits for the dampersrange in order to maintain ventilation for acceptable indoor airquality, according to the ANSI/ASHRAE 62 standard [24].

2.1. TSKFF controller

The industry standard PID controller exhibits the inability tocontrol the objectives of the HVAC system that have inherentlyadverse characteristics, such as a nonlinear, large-scale systemwitha large thermal inertia, a pure lag time, constraints and factors ofuncertain disturbances. Additionally, the indoor thermal comfortmust be decoupled from the temperature and relative humidity.Hence, fuzzy logic controllers are used due to their flexibility andintuitive use [25] in controlling the aforementioned characteristics.

2.1.1. Basic description of the control systemThe most important motivation for adopting this type of

controller is due to it being able to treat multi-controlled variablesbecause it converts a TSKFIS (TakagieSugenoeKang fuzzy inferencesystem) model into a memory layers parameters (TKS) model. Theoutput routine of the classical TSKFISmodel requires numerical andlogical operation tasks, and these tasks take a long time to becompleted. However, the TSK model uses the gradient algorithm,which is a faster online tuning method that requires less mathe-matical manipulations than other traditional methods, such as thebackpropagation method for neural networks. The most importantaspect of online tuning is that it can tune a multivariable controllerwith multiple outputs; this tuner can improve the controller'sability to deal with MIMO models that possess a large-scalenonlinear aspect, are heavily coupled, have a pure lag time,contain large thermal inertia, possess uncertain disturbance factorsand have constraints, which are common properties in HVAC sys-tems. For the purpose of this study, each strategy of the controlstructure is developed by upgraded layers of memory in order tocoordinate the modification of AHUs, which follows a change in theonline tuning system.2.1.2. Model identification architectureThe main concept of the TSKFF (Takagi-SugenoeKang fuzzy

forward) structure is based on obtaining the consequent parame-ters bymapping them from the antecedent space to the consequentspace. The obtained parameters of the consequent space areorganised as layers in the memory space. The parameters in theselayers function to the inputs of themodel. These inputs calculate theoutputs' data set, which can be clustered into seven groups within atime frame of 24 h, where each cluster for each output is repre-sented by TSK rules. The outputs Yj(X) must fit the data set. This canbe achieved by modulating the nonlinear equation for each outputyi. The modulation can be attained by tuning the parameters ai andbi. The offline tuning method is performed by using the GNMNR(GausseNewton Method for the Nonlinear Regression) algorithm,which has the capability to express the knowledge that is acquiredfrom inputeoutput data in the form of layers of parameters. TheEquation of the final model's outputs is characterised by aggre-gating the clusters' outputs and obtaining the singleton fuzzymodel, which belongs to a general class of the universal modeloutput. Subsequently, the outputs Yj(X) can be obtained as follows:

Yj X XN

iuiai

1 ebix

(1)

where X [x1, x2 xm]T is the input variables vector, i is a rulenumber subscript, ai and bi are the Tagaki-SugenoeKang parame-ters functions, ui is the basis function (weight), and j is the clusternumber subscript.

The TSK model can be structured in layers f (x; ai bi) and theweights framework that is shown in Fig. 1 where f (x; ai bi) is anonlinear function of the TSK parameters and the independentvariable x.

The TSKFF is modelled by collecting training data from thebuilding and the HVAC system equipment. Learning of the pa-rameters in the TSKFFmodel is accomplished by the offline GNMNRalgorithm. One of the advantages that the GNMNR algorithm offersis the real-time implementation of computational cost reduction.This is possible because the proposed method requires a lowernumber of iterations to perform the learning/training procedure;therefore, the tuning time will be reduced when it is implementedin real-time [5]. The controller method is realised by the TSKFF feedforward model to increase the response and time steady statecontrol for the HVAC system. Additionally, the feed forward modelis tuned online by using the gradient algorithm to enhance thestability and to reject the disturbances and uncertain factors. Byusing the gradient algorithm, a faster online tuning method is

Fig. 1. Schematic diagram of the TSK model as layers of memory.

R.Z. Homod / Energy 74 (2014) 762e774 765found that requires less mathematical manipulations than othermethods do, such as the backpropagation method for neural net-works. Themost important aspect of this online tuning is that it cantune a multivariable controller with multiple outputs [11].2.2. Decoupling problem and objectives' setting

The cooling coils in AHUs are categorised into dry andwet types.The temperature and relative humidity of air that is introduced tothe AHU that has a dry cooling coil are characterised by couplingloops due to the constant air humidity ratio. Once the temperatureis decreased, the relative humidity will be increased and vice versa.The thermal comfort can be controlled through the PMV index byusing this type of AHU, with either air temperature or air relativehumidity being a control variable (but not with both being controlvariables at the same time). The rest of the PMV variables areconsidered to be disturbances. It is desirable to control temperatureand relative humidity independently and accurately in certain in-door conditions. In these cases, the AHU with a wet cooling coil isused; both temperature and RH are varied independently based onthe flow rates of air and chilled water. It is impossible to set onevariable without affecting the other when the design of the AHUdoes not take into account the coupling dynamics between thesevariables; therefore, the importance of decoupling techniques thatare used to implement an appropriate AHU is realised.

The proposed strategy is implementing a twin cooling coil AHUand an advanced multi-variable control system. The pre-coolingcoil (wet) is equipped to cool and dehumidify the fresh air intake.The main cooling coil (dry) is used to cool the supply air. The deeplychilled water is only necessary for (pre-cooling coil) removing themoisture from the fresh air. The main cooling coil requiresmoderately cool water, according to the building load. This type oforder helps in save energy for buildings with HVAC systemsbecause higher chilled water temperatures indicate better COPs(coefficients of performance). Furthermore, the use of the PMVindex (the indoor air temperature, the radiant temperature, therelative air velocity and the relative humidity for an indoor condi-tion space) as a desired objective enables the control system tooptimise the input plant by controlling air velocity and manipu-lating the flow rate of fresh air in regard to thermal comfort levels.

The main difference between the proposed strategy and theother two strategies is in their control objectives of the operatingsystem and AHU equipment. The AHU for the conventional strategyis similar to what it is for proposed strategy, but there are twodifferences: first, it does not contain a pre-cooling coil, and second,the controlled variables include two variables that have restrictedvalues. These variables are temperature and relative humidity; bothof them are set at desired specific values. The objective of thiscontrol strategy acts as a control reference of the online tuning thatreflected negatively on its performance due to stiff references and alimited number of input plant variables that are used for optimi-sation. The controlled variables for the third (adding the reheatingcoil) strategy are similar to those of the conventional strategy, butthe difference is that the AHU is equipped with a wet main coolingcoil and a reheating coil to consolidate the controller for thedecoupling problem.

The objectives of this paper are to:

1. Assess the feasibility of using the proposed strategy in a SouthIraq climate

2. Characterise the energy savings and decoupling of the proposedsystem

3. Test the potential of the controller for multi-objective optimi-sation in the HVAC system.

These aims will be achieved by comparing three scenarios of theAHU control system in order to assess the decoupling problem andenergy savings of the simulated HVAC system.

3. Analysis of energy and mass flows of a building

The purpose of the control strategy is to minimise the totalpower consumption of the HVAC system by optimising the vari-ables of the indoor thermal comfort (i.e., the indoor air tempera-ture, the radiant temperature, the relative air velocity and therelative humidity for the indoor condition space). Generally, theelectric power consumption of the HVAC system is a function of theCOP (coefficient of performance) of the chillers, the EER (energyefficiency ratio) of the building and the cooling load of the building.The EER and COP are constants for a specified building and chiller,respectively, whereas the total cooling loads of the building vary,depending on the disturbances and the controllable variables.Therefore, the total electric power consumption can be summarisedby Equation (2) [26,27]:

EP XN

i

chlicopi

EPAHU TBCLEER

(2)

where EP is the total electric power consumption, N is the numberof chillers, chl is the chiller power, EPAHU is the electric power that isconsumed by AHU, and TBCL is the total building's cooling load.

From Equation (2), it can clearly be observed that the EP can bederived by using two different methods that are based on theenergy and mass balance equations of the building's fabric (theright term of Equation (2)) and of the AHU subsystems' equipment(the middle term of Equation (2)). Therefore, the theoriesregarding the conservation of energy and mass are applied tothermally analyse and model the overall behaviour of an HVACsystem. These theories are based on the fact that in the controlvolume of any subsystem, energy is transferred from/to a sub-system by two types of processes: mass transfer and conventional

R.Z. Homod / Energy 74 (2014) 762e774766heat transfer (conduction, convection and radiation). These pro-cesses are dominant in HVAC systems. In this research study, thesystem is subdivided into the building's and the AHU's controlvolumes. The building's energy and mass transfer can be demon-strated by Fig. 2. To evaluate the sensible heat gain of the building,the following thermal balance equation is applied to the building'scontrol volume:_Qsz}|{Cooling load

_Qair _Q furzfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflffl{Accumulation or storage of energy

_Qopq _Q fen _Q slab _Q inf _Qig;szfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{Difference between input and output of energy

(3)The term on the left side of Equation (3) denotes the output ofthe AHU, which represents the heat and mass that is transferred tothe building's control volume. On the right side of Equation (3), thefirst part (the accumulation or storage of energy) represents thethermal mass that is stored in the inner wall, indoor air andfurniture, while the second part (the difference between the inputand output of energy) represents other inputs/outputs to the con-trol volume of the building.

The latent heat gain of the building is related to the moisturetransfer, which can be evaluated by applying the conservation oftime-dependent mass law to the control volume of the building,which is shown in Equation (4);_msur;t us;t

zfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflffl{rate of moisture withdrawal by AHU dMrur;tdt

zfflfflfflffl}|fflfflfflffl{rate of moisture change

_Qig;lhfg

zffl}|ffl{rate of moisture generation

_minfuo;t _mrur;tzfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflffl{rate of moisture transfer

(4)

_mw;tcpwTwo;t Twin;t

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{energy absorbed by the coil MHecpHedTh;tdtzfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflffl{energy accumulation in the metal mass of coil

_mo;tcpaTo;t Tos;t

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{sensible energy delivered by air _mo;tuo;t uos;t

hfg

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{latent energy delivered by air dehumidification

(6)

_mw;tcpwTwo;t Twin;t

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{energy absorbed by the coil MHecpHedTh;tdtzfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflffl{energy accumulation in the metal mass of coil

_mo;tcpaTo;t Tos;t

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{sensible energy delivered by air _mcon:;thfgzfflfflfflfflfflffl}|fflfflfflfflfflffl{latent energy delivered by moisture withdrawal

(5)The term on the left side of Equation (4) is the rate of moisturethat is absorbed by the AHU. On the right side of Equation (4), thefirst part (the rate of moisture change) is the change in the rate ofair moisture in the building at time interval dt, and the other termsare related to the indoor input/output and the generated moisture.To evaluate the sensible and latent heat gains of the building, it isnecessary to calculate the left-hand sides of Equations (3) and (4),which can be obtained byapplying the laws of conservation of energyand mass to the control volume of the AHU. The AHU is subdividedinto three subsystems: the mixing air chamber, the pre-cooling coiland themain cooling coil. Energy is only consumed in the pre-coolingandmaincoolingcoils, so calculations for theenergyandmass controlvolumes are applied on these two subsystems, as follows:The term energy absorbed by the coil in Equation (5) refers tothe sensible and latent heat load that is exerted by the pre-coolingcoil. On the right side of the equation, the first term (energyaccumulation in the metal mass of the coil) refers to the rate ofchange for the heat storage in the coil mass, while the second term(the sensible energy delivered by air) refers to the sensible coolingload of the fresh air, and the third term (the latent energy deliveredbymoisturewithdrawal) refers to the latent energy that is absorbedby the coil due to the condensation of moisture. The third term onthe right side of Equation (5) can be evaluated by applying the lawof mass conservation to the air flow stream that is used for the pre-cooling coil. The following is obtained:By using the same procedure as was used for the pre-cooling coilto obtain the sensible and latent heating loads for the dynamicsubsystem equations, the main cooling coil can be written mathe-matically by using the time-dependent equation of the controlvolume, as follows:

Fig. 2. Representation of building energy and mass transfer for prototypical buildingswith HVAC systems.

_mmw;tcpwTwo;t Twin;t

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{energy absorbed by the coil MmHecpHedTh;tdtzfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflffl{

energy accumulation in the metal mass of coil

_mm;tcpaTm;t Ts;t

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{sensible energy delivered by air _mm;tum;t us;t

hfg

zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{latent energy delivered by air dehumidification

(7)

R.Z. Homod / Energy 74 (2014) 762e774 767The rate of thermal energy transfer (the sensible cooling load)from the building by the mechanical ventilation air flow (Qvent) iscalculated by using Equation (8).

_Qvent _ms;tcpaTr;t Ts;t

(8)

The power of the air supply system in the mechanical ventila-tion state (the transmission power) is mainly from the powersupply for the fan, which can be calculated by the application of thelaw of conservation of energy on the control volume of the AHU.This equation can be calculated as follows [27]:Fig. 3. The control signal percentage for the main cooling co_Q fan _ms;tcpa Ts;t To;t (9)

According to the energy balance for the indoor conditionedspace of Equation (3), the values of thermal energy flow from (1)opaque-surfaces, (2) transparent fenestration surfaces, (3) infiltra-tion, (4) indoor load and (5) ventilation are calculated by using thesteady state conditions of Equation (3), whereby all of the thermalenergy flow values are equal to the cooling load that is extracted bythe HVAC systems or the mechanical ventilation, which equals theleft-hand side of Equation (3); in turn, Equation (3) can be calcu-lated by summing Equations (6)e(9).

The instantaneous cooling load of the building can be obtainedfrom the simulation process after modelling the HVAC system.Additionally, the instantaneous cooling loads of the buildingdirectly impact the outputs of the controller signals. Therefore, themethod of calculation that is employed in this research study isbased on the output signals of the controller. The output signals ofthe controller manipulate the valves of the pre-cooling coil, themain cooling coil, the reheating coil and the dampers of the returnand fresh air to track the objective of the HVAC system. The valvesand dampers are designed according to the heating/cooling load ofthe building. The opening position of the valves and dampers isrecorded as a percentage of the fullest extent (as shown in Fig. 3)that represents the main cooling coil valve's opening position over24 h. The percentage of the opening position is related to themaximum flow rate of the valves and dampers. This signal openingposition is implemented in Matlab to obtain the energy con-sumption of the HVAC system.

The advantage of using Matlab/Simulink is in the ability to use agraphical programming language that is based on different blockcategories with different properties of each block. Matlab and itsil's chilled water valve for each of the three strategies.

Table 1Properties of the materials used for construction of the model.

Component Description Factors

Roof/ceiling Flat wood frame ceiling(insulated with R-5.3 fiberglass)beneath vented attic withmedium asphalt shingle roof

U 0.031 18 W=m2Ka roof 0.85

Exteriorwalls

Wood frame, exterior woodsheathing, interior gypsumboard, R-2.3 fiberglassinsulation

U 51 W=m2K

Doors Wood, solid core U 2.3 W=m2KFloor Slab on grade with heavy carpet

over rubber pad; R-0.9 edgeinsulation to 1 m below grade

Rcvr 0.21 W=m2K;Fp 85 W=m2K

Windows Clear double-pane glass inwood frames. Half fixed, halfoperable with insect screens(except living room picturewindow, which is fixed). 0.6 meave overhang on east and westwith eave edge at same heightas top of glazing for allwindows. Allow for typicalinterior shading, half closed.

Fixed: U 2.84 W=m2K;SHGC 0.67.Operable: U 2.87 W=m2K;SHGC 0.57; Tx 0.64;IACcl 0.6

Construction Good Aul 1.4 cm2/m2

R.Z. Homod / Energy 74 (2014) 762e774768toolboxes are adopted to perform all of the identification processesand simulations in this work, as well as in our previous works[28e31]. System identification and control system toolboxes wereused to identify and build the model, while the fuzzy logic toolboxwas used for the TSK model identification. The obtained models arethen introduced in the Matlab/Simulink environment for simula-tion and analysis. These categories include the input/output,transfer functions, arithmetic functions, state space models anddata handling. The building model is represented in the form ofODE (ordinary differential equation) solvers, which are automati-cally configured during the Simulink model's run-time. The algo-rithm of the controller is designed by using Matlab m-files,parameter layer memory and S-functions, which are based on on-line parameter tuning. The technique for calculating the coolingloads is easily implementable, whereby the thermal balanceFig. 4. Matlab blocks for the simuequation is derived from the arithmetic functions, from which theenergy consumption can be obtained.4. Simulation results and discussion

4.1. Physical and theoretical model description

The simulated building model is a typical one-story house witha simple structure. The house consists of heavyweight construction(brick and concrete) that measures 4.5 m in height, with 248.6 m2

of gross ground floor area. The net floor area of the entire building is195.3 m2, excluding the garage area; the gross exposed area of thewindows and wall is 126.2 m2, while the net area of the exteriorwall is 108.5 m2. The overall volume of the house, excluding thegarage and suspended ceiling space, is 781.2 m3 Table 1 shows thephysical properties of the components of the building. The dry bulbtemperature varies according to the spring season's climate inBasrah city, which ranges from 18 C to 32 C, and the humidityratio varies from 0.01 to 0.01909 kg of moisture per kg of dry air. Thebuildingmodel's transfer function and the PMV, or thermal comfortsensor model, are presented in Appendices A and B [32,33].

To reduce the design cost, as well as the cost that is needed tofabricate the three HVAC systems, simulation methods are imple-mented in order to test and analyse the results. The identificationapproach of the model demonstration is based on the multi-zonemodel of the RLF (residential load factor) method. The identifiedmodel is simulated by three different controller strategies in orderto study their levels of indoor thermal comfort and energy con-sumption. The first system is a conventional control system (thecontrol variable objectives are temperature and relative humidity).The second is a conventional system that includes the addition of areheating coil and awet main cooling coil, while the third system issimilar to the first system, but it includes an addition of a pre-cooling coil and a PMV index in order to measure the objective ofthe controller. The three types of systems are run together in orderto study their performance and energy consumption (as shown bythe simulation block diagram in Fig. 4, which presents the simu-lation in the evaluation of performance and energy consumption ofthe three systems).lations of all three systems.

Fig. 5. PMV comparisons of the results of the three different systems with different objectives and designs.

R.Z. Homod / Energy 74 (2014) 762e774 769The mean radiant temperature is a more complicated quantitythat depends on the temperature of the surrounding surfaces, aswell as on angle factors of the surrounding surfaces. Therefore, theplant model leads to the output of the plug-in model of the PMVindex, except the mean radiant temperature requires an interme-diate sub-model where its output is taken into account because it isone of the main factors that affects thermal comfort. This sub-model estimates the mean radiant temperature by using twomethods: theoretical and numerical. For the theoretical method,the mean radiant temperature is estimated from the measuredtemperature of the surrounding walls and surfaces and the anglefactors of these surrounding surfaces. All of the indoor surfaces areassumed to be black because most building materials have a highemittance , and it is assumed that small temperature differencesexist between the surfaces of the enclosure (i.e., linear combinationof system states). Therefore, the following equation is used [34]:

MRT T1FP1 T2FP2 / TnFPn (10)

whereMRT is theMean Radiant Temperature, Tn is the temperatureof surface n and Fp-n is the angle factor between a person andsurface n.Fig. 6. Indoor temperature comparisons of the results between theFor the numerical estimation, a black-globe thermometer sensoris used.

4.2. Decoupling results and discussion

The plant model is dynamically subjected by many thermaldisturbance factors, such as the K2 solar radiation, f4 inside sensible,FDR fenestration, etc. Three simulation sets are conducted over 24 hand include nominal, noise and sensor deterioration, as well as anuncertainty operation, for the three systems' behaviours to beobserved and studied for the different conditions. The mainobjective of this work is to validate the decoupling of the proposedstrategy.

4.2.1. Nominal operating conditionsPre-cooling coils are added to the proposed AHU of the HVAC

system in order to economically control the indoor relative hu-midity in a humid climate. Additionally, the proposed system hasfour control variables for an indoor conditioned space (i.e., theindoor-air temperature, the indoor-air velocity, the indoor-air hu-midity and the flow rate of fresh air). These control variables areoptimised by the controller to provide economical indoor-airthree different systems with different objectives and designs.

Fig. 7. Indoor relative humidity comparisons of the results between the three different systems with different objectives and designs.

R.Z. Homod / Energy 74 (2014) 762e774770conditioning that yields the desired level of thermal comfort andindoor air quality, according to ASHRAE and ISO standards; this alsoreduces the cooling load during implementation in real-time. Theother systems have two control objectives that are set at certaindesired values for the indoor conditioned space (i.e., the indoor-airtemperature and the indoor-air humidity). Fig. 4 shows the threedesigns of the HVAC system. The manipulation of each TSKFF forthe five AHU inputs in the three designs of the HVAC system be-haves differently. In the proposed system, the input feedbacksensor allows some degree of tolerance instead of requiring aspecific value for the temperature and relative humidity, which isneeded in the conventional HVAC systems. This optimisationovercomes the coupling effects (temperature and relative humidi-ty) perfectly by providing the desired level of thermal comfort,which is shown in Fig. 5. In regard to Fig. 5, it can be observed thatthe proposed (the model of the PMV index addition) system cantrack the desired objective and can achieve outstanding perfor-mance, while the systemwith the added reheating coil acts withinan acceptable thermal comfort range that has an acceptable offsetFig. 8. PMV comparisons of the results of the three different systems bafrom the set point. The conventional system was found to violatethe ASHRAE Standard 55-92 [35] and ISO-7730 [36] for the desiredlevel of indoor thermal comfort. These standards recommend thatthe acceptable levels of thermal comfort are limited to a rangebetween0.5 < PMV < 0.5. It is evident that this violation is causedby the coupling of temperature and relative humidity. The tem-perature curves of all three systems are similar to the PMV trendthat is shown in the simulation results, which are tabulated inFig. 6. The periodic effect of the coupling factors is apparent from05:30 o'clock to 08:00 o'clock and from 19:00 o'clock to 24:00o'clock. The effect can be more clearly observed in the behaviour ofthe relative humidity (as shown in Fig. 7), in which the conven-tional system fluctuates within a wide range, whereas the othersystems remain in the range of approximately 50% RH. The rec-ommended range of RH, according to the ASHRAE Standard 55-92and ISO-7730 for the indoor comfort condition, is 40%e60%. Highhumidity not only causes poor indoor air quality, but it also causeswood decay, metal corrosion and structural deterioration [37]. Thecalculations of energy consumption are based on the controllersed on the operating conditions of noise and sensor deterioration.

Fig. 9. PMV comparisons of the results of the three different systems in regard to the operating conditions when model uncertainties are present.

R.Z. Homod / Energy 74 (2014) 762e774 771signals. One of these signals is shown in Fig. 3. Fig. 3 shows theresults of the simulation of the control signal variation for the maincooling coil of the chilled water valve, with respect to time. In Fig. 3,the signals for the conventional system with a reheating coil actssimilar to a BangeBang control action. The modulating valvecontinuously fluctuates between ON-OFF, which will eventuallywear out the valve and shorten its lifespan. It can be clearlyobserved that the proposed system signal works very efficiently,which provides good control performance. Figs. 5e7 show thetransient response for the initial condition. This took approximatelyan hour because the plant model is dynamically affected by thethermal mass of the building structure and slab floors, which cre-ates a flywheel effect. The influence of this flywheel effect begins tofade and becomes less intense after the HVAC system starts, whichcan clearly be observed in the signal of valve open position in Fig. 3.4.2.2. Operating conditions of noise and sensor deteriorationDisturbance mode has tested decoupling through its validation

of the rejection of noise and sensor deterioration. In noise andsensor deterioration, the controlled process parameters, sensors'Fig. 10. Psychometric chart comparisons of the results of the three different systemgains, and noise signals are able to change in the same manner foreach system and simulation that is conducted. Here, we supposethat sensors deteriorate with 20% fault, and the sensors' gain ischanged to 0.8 (sensor gain 1 when the sensor performance is100%). Additionally, to test the sensitivity of the proposed methodto noise, each system is subjected to the same noisy environmentby adding a 10% NSR (noise-to-signal ratio), which refers to theratio of the continuous noise signal to the controlled signal. Thesensor deterioration set and subjected noise signal are applied atthe start of the simulation. Fig. 8 shows the three different systemsto try to track a PMV set point, which changes under a square wavefrom 0.4, 0 and 0.4 during a 24 h time frame. By using this test,one can clearly observe the three systems' behaviour for the PMV,where the proposed system provides superior control performanceand does not violate the ASHRAE 55-92 and ISO-7730 Standards. Incontrast, the other systems exhibited deterioration in their per-formances and, consequently, violated the Standards of the indoorthermal comfort. Thus, the proposed system achieves significantresults that verify the use of decoupling parameters rather thanadding a reheating coil or using conventional decoupling methods,s in regard to the operating conditions when model uncertainties are present.

Fig. 11. A comparison of the energy consumption results based on the cooling coil load variation between the three different schemes.

R.Z. Homod / Energy 74 (2014) 762e774772which are extremely intricate and too impractical to solve numer-ically when the plant system model is complicated, which is thecase for HVAC systems.

4.2.3. Operating conditions regarding the presence of modeluncertainties

In regard to robustness validation, the plantmodel encompassesa wide range of operating parameters, which vary as the HVACsystems undergo fluctuating loads due to changes in external dis-turbances during a typical day's operation. Therefore, in the pres-ence of uncertainties regarding themodelling of such parameters, itbecomes necessary to use a robust intelligent controller, such asTSKFF, to obtain efficient operation in the HVAC systems. To vali-date the robustness of the TSKFF controller, the building heat losscoefficients, the heat transfer coefficients of the fan-coil units andpumps and the thermal time constant are changed. Before asimulation run begins, all of the model coefficients and the timeconstant are increased by 20%. Three TSK models of controllers aretuned based on the nominal plant model and then are integratedinto a control algorithm that manipulates AHU parameters toFig. 12. A comparison of the results of the power consumcontrol indoor thermal comfort. The conventional strategy leads totheworst indoor ambient conditions and becomes less intense afteradding a reheating coil, which is shown in Fig. 9. However, bothstrategies violate the standard limitation of ASHRAE 55-92 and ISO-7730. The proposed scheme maintains asymptotic tracking of agiven reference signal, and it occurs in the presence of the sameparameter variations and model uncertainties when it does not usereheating and wet main cooling coils. For validation, psychometriccharts are the most commonly used tool for indoor studies andoutdoor air conditions. Fig. 10 describes the air states cycle ofphysical and thermodynamic properties for indoor conditions over24 h. The proposed strategy seems to satisfy the TCZ (thermalcomfort zone), whereas the other strategies frequently crossoverTCZ.

4.3. Energy saving results and discussion

The purpose of the model of the PMV index addition to theproposed system is to change the restricted conventional objectivevariables (temperature and relative humidity) of an HVAC system inption between the three different system schemes.

R.Z. Homod / Energy 74 (2014) 762e774 773addition to increasing its flexibility with respect to the indoorcontrol parameters (temperature, fresh air flow rate, indoor airvelocity and relative humidity). The model of the PMV indexaddition also enables the controller to improve its performance.The TSKFF controller exploits the flexibility of the control param-eters by optimising the parameters through the manipulation ofthe AHU parameters (inputs) to provide the desired levels of ther-mal comfort, while simultaneously reducing the energy con-sumption of the HVAC system.

Furthermore, the velocity of indoor air can reduce the coolingload. This can be observed from the simulation results of the energythat is consumed by the cooling coil load, which is shown in Fig. 11.The simulation techniques that are used to calculate the coolingloads are straight forward: thermal balance equations are imple-mented by using arithmetic functions, and then the consumedenergy can be obtained by using Equations (6)e(9). The simulationresults of the consumed energy in the systemwith a reheating coilreveal higher energy consumption than the consumption of theother systems because the cooling process reduces the air tem-perature to the sub-cooling state before the reheating processovercomes the coupling effect and meets the demands of indoorthermal comfort.

Although the conventional system is better in terms of energysavings than the system that has the addition of a reheating coil, theconventional system does not meet the desired level of indoorthermal comfort. However, the proposed system shows morefavourable results than the other two systems with respect toachieving the desired level of thermal comfort and reducing energyconsumption, simultaneously. Based on Fig. 11, it can be observedthat the differences in energy consumption among the three sys-tems increase during the times periods that include the presence ofa coupling effect. The average power consumption for the threedifferent systems (the conventional system, the addition of areheating system and the proposed system) are 10.713 kW,13.27 kW and 9.016 kW, respectively. The average power costs thataccompany the addition of a reheating system are 1.4718 timeshigher than that of the proposed system. Based on the data for 24 hof power consumption, the calculations for energy consumption foreach of the three strategies show that energy consumption in theproposed strategy is 32.06% lower than the system with an addedreheating coil, which is shown in Fig. 12. This result closely matchesthe results that were obtained by Yang and Su [38] in which anintelligent controller was developed to adjust the PMV index,which led to saving approximately 30% more of the energy con-sumption than the conventional methods. Furthermore, the simu-lation of the cooling coil load output is compared to the numericalresults, which are based on the CLF/CLTDC (the cooling load factorfor the glass/corrected cooling load temperature difference)method [39,40]. The calculation considers the effects of numerousoutdoor environmental parameters on the indoor thermal loads.The cooling load of the building is calculated every 30min to obtainthe absolute margin of error between the simulation results (theproposed system) and the numerical calculation, which was foundto vary between 0.064 and 0.107 kW. To have a clearer assessmentof the error between the simulation and numerical calculations ofthe cooling coil load output, in this study, the statistical index of thecoefficient of determination (r2) was calculated based on Equation(11) and had a value of r2 0.974.

r2 NP

yiyi P

yiP

yi2hNP

y2i P yi2

NP

y2i P yi2

i (11)

where yi is the numerical result, yi is the simulation result, and N isthe number of test samples.5. Conclusion

In this context, the simulation results of the comparison inves-tigated the use of a PMVmodel input as an objective optimisation ofcontroller decoupling and of reductions in the energy consumed byan HVAC system. This was performed by considering all of thefactors that are affected by indoor thermal comfort, which are re-flected by the PMV index. The control system that is proposed inthis work includes, as part of its structure, a PMV model for theoptimisation of the deviations in the parameters of indoor thermalcomfort and of the generation of control actions that pertain to theAHU inputs. The task regarding the PMV index output is, therefore,to acquire controller output signals more accurately by exploitingthe decision algorithm's flexibility for the PMV index input's ag-gregation. The weather in Basrah, a southern city of Iraq, wasconsidered as a case study to test the system. The output controllersignals were adopted to obtain the energy consumption for threedifferent control objectives and strategies, which were evaluatedwith respect to typical and modified HVAC systems. Based on theresults of the performed simulations, we can conclude that whenusing the indoor PMV as a variable objective for the HVAC system,the controller performs better and provides more energy savings,while still attaining the desired level of indoor thermal comfort.The multi-input of the AHU is manipulated by the TSKFF controller,which is characterised by the optimisation of the outputs for energysavings. Both the conventional and proposed systems show thatenergy is saved in comparison to the system that has an additionalreheating coil in the AHU. The conventional HVAC system shows asavings of up to 19% of energy usage, which is 61.5 kWh/d less thanthe energy that is used by an HVAC system that has an additionalreheating coil; in contrast, the proposed strategy can save up to32.06% of energy usage, which is 102.1 kWh/d less than the energythat is used by the HVAC system that has an additional reheatingcoil. This is because a TSKFF that is equipped with a model of PMVindex reduces the energy consumption of a building by as much asit can by utilising the outdoor climate in controlling the rate of freshair flow. Meanwhile, the conventional control strategy adjusts thetemperature and relative humidity to a predefined strict set point,which does not allow for optimisation of energy consumption forindoor thermal comfort. An important finding of this study is thatthe proposed strategy economically addressed the coupling prob-lem in addition to providing the desired level of thermal comfort.Furthermore, the procedure of using the PMV model and TSKFF isstraightforward and easy to implement.Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.energy.2014.07.047.References

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Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-h ...1 Introduction2 Control system design2.1 TSKFF controller2.1.1 Basic description of the control system2.1.2 Model identification architecture

2.2 Decoupling problem and objectives' setting

3 Analysis of energy and mass flows of a building4 Simulation results and discussion4.1 Physical and theoretical model description4.2 Decoupling results and discussion4.2.1 Nominal operating conditions4.2.2 Operating conditions of noise and sensor deterioration4.2.3 Operating conditions regarding the presence of model uncertainties

4.3 Energy saving results and discussion

5 ConclusionAppendix A Supplementary dataReferences

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