Predictive pre-cooling of thermo-active building systems...

UHVC_A_643752 hvcxml-v3.cls August 8, 2012 19:57

Predictive pre-cooling of thermo-active building systemswith low-lift chillers

N. T. Gayeski,1,2,∗ P. R. Armstrong,3 and L. K. Norford41Building Technology Program, Massachusetts Institute of Technology, Rm 5-418, 77 Massachusetts Ave.,

Cambridge, MA 02139, USA52KGS Buildings, LLC, Cambridge, MA, USA

3Mechanical Engineering Program, the Masdar Institute of Science and Technology, Abu Dhabi, United ArabEmirates

4Department of Architecture, Massachusetts Institute of Technology, Cambridge, MA, USA∗Corresponding author e-mail: [email protected]

This article describes the development and experimental validation of a data-driven model predictivecontrol algorithm that optimizes the operation of a low-lift chiller, a variable-capacity chiller run atlow pressure ratios, serving a single zone with a thermo-active building system. The predictive controlalgorithm incorporates new elements lacking in previous chiller pre-cooling control optimization methods,including a model of temperature and load-dependent chiller performance extending to low-pressureand part-load ratios and a data-driven zone temperature response model that accounts for the transientthermal response of a concrete-core radiant floor thermo-active building system. Data-driven models ofzone and concrete-core thermal response are identified from monitored zone temperature and thermal loaddata and combined with an empirical model of a low-lift chiller to implement model predictive control.The energy consumption of the cooling system, including the chiller compressor, condenser fan, andchilled-water pump energy, is minimized over a 24-h look-ahead moving horizon using the thermo-activebuilding system for thermal storage and radiant distribution. A generalized pattern-search optimizationover compressor speed is performed to identify optimal chiller control schedules at every hour, therebyaccomplishing load shifting, efficient part-load operation, and cooling energy savings. Results from testingthe system’s sensible cooling efficiency in an experimental test chamber subject to the typical summer weekof two climates, Atlanta, GA, and Phoenix, AZ, show sensible cooling energy savings of 25% and 19%,respectively, relative to a high efficiency, variable-speed split-system air conditioner.

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Introduction30

A low-lift cooling system combines a low-liftchiller (a variable-capacity chiller that operates ef-ficiently at low pressure ratios and over a wide ca-pacity range), radiant cooling with variable-speed

Received January 4, 2011; accepted October 25, 2011Nicholas T. Gayeski, PhD, is Research Affiliate at Massachusetts Institute of Technology and Partner at KGS Buildings. Peter R.Armstrong, PhD, is Associate Professor. Leslie K. Norford, PhD is Professor.

distribution, predictive pre-cooling of thermal en- 35ergy storage (TES), and a dedicated outdoor airsystem (DOAS) for ventilation and dehumidica-tion to achieve low-energy cooling (Jiang et al.2007; Armstrong et al. 2009a, 2009b; Katipamulaet al. 2010). Efficient operation of a low-lift chiller 40

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HVAC&R Research, 18(5):1–16, 2012. Copyright C© 2012 ASHRAE.ISSN: 1078-9669 print / 1938-5587 onlineDOI: 10.1080/10789669.2012.643752


2 VOLUME 18, NUMBER 5, OCTOBER 2012

is enabled through predictive pre-cooling of TES,such as a thermo-active building system (TABS).The chiller operates at lower average lift condi-tions through lower part-load operation overnightand higher chilled-water temperatures for radiant45TABS distribution, and thus higher average chillerefficiencies (Gayeski et al. 2010). Extensive simu-lation of low-lift cooling systems has shown sig-nificant potential annual cooling energy savings ina range of climates and building types relative to50conventional variable air volume (VAV) systems(Armstrong et al. 2009a, 2009b; Katipamula et al.2010). For typical buildings, Katipamula et al.(2010) found that simulated annual cooling energysavings relative to VAV systems with conventional55two-speed chillers ranged from 37% to 84%, de-pending on the climate and building type. Thesesimulations assume ideal thermal storage, not realthermal storage such as TABSs.

This article describes the development of a data-60driven model predictive control algorithm that ac-counts rigorously for the TABS transient responseand optimizes control of a low-lift chiller used topre-cool TABS-TES. The pre-cooling control algo-rithm has been applied to a low-lift chiller serving an65experimental test chamber with a TABS radiant floorsubjected to two typical summer week climate con-ditions. The algorithm integrates for the first timea temperature- and load-dependent low-lift chillerperformance model with data-driven temperature70response models of zone and a TABS to optimizesensible cooling system performance through pre-dictive pre-cooling control. The performance andoptimization of the DOAS component of a low-liftcooling system can be treated separately, assuming75that the DOAS includes its own efficient variable-capacity direct expansion (DX) cooling or other ef-ficient dehumidification separate from the low-liftchiller plant serving the TABS.

The sensible cooling energy performance of the80low-lift cooling system with optimized pre-coolingof TABSs is compared to that of a high-efficiency,variable-capacity split-system air conditioner serv-ing the same experimental chamber. These two sys-tems have been chosen for experimental compari-85son as a subset of the eight system configurationssimulated by Armstrong et al. (2009a, 2009b) andKatipamula et al. (2010) comprising all combina-tions of the following subsystem alternatives:

� a two-speed chiller or variable speed chiller,90� a VAV system or a radiant cooling system with a

DOAS, and

� TES with predictive pre-cooling control or noTES and no pre-cooling control.

The variable-capacity split-system air conditioner 95serving the experimental test chamber is similar tothe radiant system with variable-speed chiller andpassive TES simulated in this previous research be-cause the fan power of the ductless indoor unit isvery small (0.1076 W/L/s [0.05 W/CFM] at high 100speed).

The research presented here advances the stateof the art in two important ways. First, a predictiveTABS pre-cooling control algorithm is developedto control a low-lift chiller that accounts for the 105TABS temperature response and its effect on chillerefficiency. Second, a low-lift cooling system is testedexperimentally for the first time.

Literature review 110

Predictive control to pre-cool TES has been stud-ied with a variety of system configurations andoperating modes. Topics addressed in the litera-ture include pre-cooling of discrete-active TES,such as ice-storage or stratified chilled-water tanks; 115instrinsic-passive storage, such as building thermalmass; and intrinsic thermo-active TES, such as aTABS.

Traditional, intrinsic-passive TES applicationsuse conventional cooling equipment such as VAV 120systems to sub-cool zones and thereby pre-coolbuilding thermal mass from zone air (Eto 1984;Brandemuehl et al. 1990; Conniff 1991). TABS ther-mal storage utililizes pipe embedded in the buildingstructure to actively charge building thermal mass, 125which then passively absorbs heat from occupiedzones over the day subject to the temperature re-sponse of both the zone system and TABS.

Pre-cooling strategies for intrinsic-passive TESoften involve a schedule of zone temperature set- 130points and/or pre-cooling rates for conventionalVAV or other air handling systems. The schedulesattempt to reduce peak power demand or minimizeenergy cost or consumption. Peak load reductionby passive pre-cooling of TES through schedul- 135ing zone set-points has been extensively studied(Snyder and Newell 1990; Rabl and Norford 1991;Keeney and Braun 1996; Braun and Chaturvedi2002; Braun and Lee 2006; Roth et al. 2009) but theimpact of pre-cooling on chiller performance has 140not. Henze et al. (1997, 2004) optimized zone set-points and a discrete-active TES pre-cooling control


HVAC&R RESEARCH 3

schedule based on two constant coefficients of per-formance (COPs) to account for the difference inchiller COP during chilled-water and ice-making145operation. Then studies appeared regarding the im-pact of forecasting uncertainty (Henze et al. 1999),adaptive thermal comfort criteria (Henze et al.2007), energy and demand charges and other util-ity rate structures (Braun 2007; Henze et al. 2008),150and simplified optimization methods (Henze et al.2010). However, none of the research above ac-counts rigorously for the temperature- and load-dependent performance of variable-speed chillersthat are highly efficient at part load, which may155greatly enhance the energy efficiency of pre-coolingstrategies (Jiang et al. 2007; Armstrong et al. 2009a,2009b; Katipamula et al. 2010).

Braun (1990) and Kintner-Meyer and Emery(1995) presented pre-cooling control optimization160methods in which the temperature and part-load-dependent performance of conventional chillerswere taken into account. Chiller performance isa function of condensing, evaporating, and part-load conditions; however, the modeled chiller165performance did not reflect more efficient part-load and low-pressure-ratio operation now possi-ble with high-efficiency variable-capacity chillers.Armstrong et al. (2009a, 2009b) presented an ap-proach in which semi-empirical component-based170models of low-lift variable-capacity chillers are usedto optimize the control of a low-lift chiller serv-ing idealized TES in simulation. Armstrong et al.(2009a) simulated low-lift cooling systems in fiveclimates and reported significantly more potential175cooling energy savings than previous pre-coolingstrategies. largely because of improved low-lift part-load chiller performance. However, those authorsdid not fully account for the transient response ofintrinsic-active TES, such as a TABS, and its impact180on the performance of the low-lift chiller.

Effective control of cooling through TABSsand its potential for cooling energy savings arean open area of investigation (Doebbler et al.2010). TABSs are most effective in buildings with185high-performance envelopes and moderate loads(Brunello et al. 2003; Lehmann et al. 2007) andrequire careful humidity control, such as through aDOAS (Adlam 1948; Mumma and Shank 2001), andconcrete surface or chilled-water temperature con-190trol to prevent condensation. Olesen et al. (2002)presented a study of control concepts for TABSsthat focused primarily on the timing and duration ofcooling the concrete core relative to thermal comfort

and pumping energy consumption. Recent develop- 195ments in TABS control have focused on room tem-perature feedback and pulse-width modulated pumpoperation to further reduce pumping energy and im-prove comfort (Guntensperger et al. 2005; Gwerderet al. 2009). None of the foregoing TABS control 200strategies accounts for the performance of the chillerserving the TABS, and only recently has simplifiedzone temperature feedback been incorporated intothe control (Gwerder et al. 2009).

Low-lift predictive pre-cooling 205

control for TABS

This article presents a model-based predictivecontrol algorithm for a TABS served by low-liftchillers that incorporates zone and TABS thermalresponse models as well as a low-lift chiller per- 210formance model into the control. TABSs are partic-ularly appropriate for low-lift cooling systems be-cause of the following:

� TABSs require only moderate temperature chilledwater; 215

� TABSs have high thermal storage efficiency, de-fined as the magnitude of stored cooling energyextracted for cooling relative to the magnitude ofcooling energy delivered to storage; and

� TABSs operate with very low transport energy 220costs.

A framework for optimal control of low-liftchillers to pre-cool TABS is presented that deter-mines an optimal control schedule at each hour,looking ahead 24 hours. A 24-h look ahead is com- 225mon in pre-cooling control algorithms, because av-erage chiller efficiency can be enhanced by loadshifting relative to the diurnal cycle of outdoor tem-perature and cooling loads (Krarti et al. 1999). Insome cases, especially in the case of discrete TES 230where charging and discharging rates can be con-trolled and when demand charges are taken intoaccount, longer prediction horizons may be appro-priate. However, when TES consists only of a TABS,the prediction horizon is limited in practice by the 235capacity of TABS-TES and limited control over dis-charge rates for stored cooling energy.

The control algorithm presented here minimizescooling energy consumption (or cost) over 24 hoursby controlling chiller compressor speed and con- 240denser fan speed in a near-optimal way. The objec-tive function includes a model of a variable-capacity



chiller, presented in Gayeski et al. (2010), thataccounts for the temperature- and load-dependentchiller power consumption and cooling rate. It also245includes transfer function models of concrete-coretemperature response and zone temperature reponse(Seem 1987; Armstrong et al. 2006a, 2006b). Theobjective function for the model predictive controloptimization can be described mathematically as250follows:

arg minω J =24∑

N=1

(rN PN + ϕPON + PEN ), (1)

where J is the sum over 24 hours of the coolingsystem energy consumption (or cost), a penalty foroperative temperatures outside of a comfort range,and a penalty for chiller evaporating temperatures255below a low temperature threshold. The variablesare as follows:

� rN is a weighting factor set to one to minimizeenergy consumption or a utility rate to minimizecost;260

� PN is the average power input to the coolingsystem during hour N , which is a function ofoutdoor temperature, evaporating temperature(which is a function of concrete-core tempera-ture), and the optimal compressor and condenser265fan speeds at N ;

� ϕ is a weighting factor to penalize excursionsfrom an allowable operative temperature region;

� PON is a penalty function of zone operative tem-peratures To,N at time N , which is a function of270current and past thermal loads and temperaturesas described below; and

� PEN is a penalty function of chiller evaporatingtemperature relative to a low temperature thresh-old, which prevents predicted controls that would275cause freezing.

Models for chiller performance and zone andconcrete-core temperature response implicit inEquation 1 are described in the following sections.

Chiller performance model280

The cooling system energy consumption Pn in-cludes the energy consumption of the water circula-tion pump and the low-lift chiller serving the TABSand is given by the following equation:

PN = Ppump,N + Pchiller,N (Tx,N , Te,N , ωN ,

f (Tx,N , Te,N , ωN )), (2)

where Ppump,N is the energy consumption of the 285chilled-water pump over the hour N , and Pchiller,N

is a regression-based curve-fit model of the powerconsumption of a low-lift chiller.

The chiller power consumption at hour N ,Pchiller,N , is shown in Equation 3. It is a tri-cubic 290in evaporating temperature Te, outdoor air tempera-ture Tx, and compressor speed ω, with five additionalterms involving condenser fan speed f .

Pchiller,N

=

⎛⎜⎜⎜⎜⎜⎜⎜⎝

c1 + c2Te + c3Tx + c4ω + c5T 2e

+ c6T 2x + c7ω

2 + c8TzTe + c9Teω

+ c10Txω + c11T 3e + c12T 3

x + c13ω3

+ c14T 2e Tx + c15T 2

e ω + c16T 2x Te

+ c17T 2x ω + c18ω

2Te + c19ω2Tx

+ c20TeTxω + c21 f + c22 f 2

+ c23 f Te + c24 f Tx + c25 f ω

⎞⎟⎟⎟⎟⎟⎟⎟⎠

N

(3)

The coefficients of this model can be determinedfor variable-capacity chillers through regression 295based on physics-based performance simulationsor measurements of actual chiller performance.Models of the same form as Equation 3, but withdifferent coefficients, can be identified to representcooling capacity QCchiller,n and electric input ratio 300(EIR) EIRchiller,n as functions of Te, Tx, ω, and f .These models have been identified in a calibratedtest stand for the same manufacturer and modelof variable-capacity chiller/heat pump used in thefollowing described experiments. The identified 305models for Equation 3 fit measured power, coolingrate, and EIR with model accuracies of 5.5% or lessdown to pressure ratios of 1.2 (Gayeski et al. 2010).Models identified from measured data should notbe assumed to be valid outside of the range of 310conditions tested experimentally. Curve-fit models,suitable for integration in a predictive control al-gorithm, can also be identified from physics-basedmodels of chillers (Zakula 2010) that may be gener-ated by simulating a particular system configuration 315given the capacity and configuration of each com-ponent and a suitable range of operating conditions.

Zone and concrete-core temperatureresponse models

The presence of Te in Equations 2 and 3 requires 320that evaporating temperature be estimated at eachtime step of the 24-h optimization. The predictionof Te may be based on engineering calculationsor data-driven models relating the chilled-water


HVAC&R RESEARCH 5

supply or return temperatures and the chilled-water325flow rate to chiller evaporating temperature at spe-cific operating conditions. For a given chiller with agiven evaporator water flow rate, a given compressorspeed, and a given closed-loop superheat control al-gorithm, Te is directly related to chilled-water return330temperature Tchwr (Armstrong et al. 2009b).

Gayeski (2010) showed that Tchwr can be pre-dicted based on past cooling rates, return watertemperatures, and concrete-core temperature Tcc us-ing a simple second-order transfer function model335for Tchwr, equivalent to a second-order thermal RCQ1

model, as a function of cooling rate QCchiller andconcrete-core temperature Tcc measured at top-of-tube elevation. This model is shown in Equation 4:

Tchwr,N =N−1∑

n=N−2

anTchwr,n +N∑

n=N−2

bnTcc,n

+N∑

n=N−2

cnQCchiller,n. (4)

An application of comprehensive room transfer340function (CRTF) models (Seem 1987; Armstronget al. 2006b) can be used to predict zone operativetemperature and concrete-core temperature Tcc inEquation 4 (Gayeski 2010). A CRTF is a combina-tion of two or more conduction transfer functions345(Stephenson and Mitalas 1967, 1971) that predictscooling loads from zone temperatures, outdoor tem-peratures, and thermal loads (Armstrong et al 2006a;Seem 1987). Temperature CRTFs are complemen-tary to CRTFs and predict zone temperatures from350cooling rates, outdoor temperatures, and thermalloads, rather than predicting cooling loads. Physicalconstraints on the coefficients of temperature CRTFmodels have been presented by Armstrong et al.(2006b) that resulted in causal, stable, and gener-355ally more reliable models than black-box models.

In low-lift predictive pre-cooling of the TABS,the operative temperature To is predicted from thefollowing M th-order temperature CRTF model:

To,N =N−1∑

n=N−M

onTo,n +N∑

n=N−M

pnTx,n +N∑

n=N−M

qnTa,n

+N∑

n=N−M

rnQIn +N∑

n=N−M

snQCchiller,n. (5)

The temperature of the concrete-core Tcc is pre- 360dicted from a similar temperature-CRTF model:

Tcc,N =N−1∑

n=N−M

dnTcc,n +N∑

n=N−M

enTx,n

+N∑

n=N−M

fnTa,n +N∑

n=N−M

gnQIn

+N∑

n=N−M

hnQCchiller,n. (6)

In Equations 5 and 6, To is the zone operative tem-perature, Tcc is the concrete-core temperature, Tx isthe outdoor air temperature, Ta is an adjacent zonetemperature (multiple zones in general but in the ex- 365periment only one), QI is the internal heat load, andQCchiller is the cooling rate delivered by the low-liftchiller. The lowercase letters are CRTF coefficientsfor each variable at each time step n into the past.

The operative temperature To,N and concrete- 370core temperature Tcc,N at the next time step N arepredicted from measurements of each variable at theprevious timesteps N – M to N – 1 and forecasts ofexogenous variables at timestep N . A number, Z –1, of adjacent zones may be incorporated by creat- 375ing Z CRTF models and solving for Z zone opera-tive temperatures. The choice of chiller compressorspeed at each hour of the 24-h look-ahead controlschedule determines the cooling rate and, thus, zoneoperative temperature, concrete-core temperature, 380chilled-water temperature, evaporating temperature,chiller power consumption, and chiller cooling rateat each hour of the next day.

Operative temperature comfort penalty

The second term in Equation 1 accounts for zone 385operative temperature comfort constraints. The op-erative temperature penalty is given by the followingequation:

ϕPON

=

⎧⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎩

ϕ((To,min + 0.5)−To,N )2 To,n ≤ To,min + 0.5

0 To,min + 0.5 < To,n

< To,max − 0.5

ϕ((To,N − (To,max

−0.5))2 To,nTo,max − 0.5

;

(7)



To,min and To,max are the minimum and maximumallowable operative temperatures, and To,N is the390operative temperature at the current timestep N .Operative temperatures outside of and within 0.5◦C(0.9◦F) of the comfort bounds based on ASHRAEStandard 55 (ASHRAE 2007) are penalized, with aquadratically increasing penalty moving away from395the comfort bounds so that the derivatives are contin-uous at the boundaries. The weight ϕ penalizes op-erative temperature excursion relative to power con-sumption. A choice for ϕ greater than the minimumchiller power consumption at a given speed will400cause an operative temperature penalty greater thanthe cost of running the chiller during that hour whenoperative temperature exceeds comfort bounds by0.5◦C (0.9◦F). The relative humidity of the zoneis not included in the pre-cooling objective func-405tion because humidity is controlled separately bya DOAS and does not take part in the pre-coolingoptimization.

Chiller operational constraint penalty

The last term in the objective function, PEn, is a410constraint on the evaporating temperature Te of therefrigerant to prevent control actions (cooling rates)at future time steps that would cause the chiller tofreeze. The constraint Te,min can be chosen conser-vatively to prevent Te below 1◦C (1.8◦F). The evap-415orating temperature penalty function is as follows:

PEn ={

0 Te(Tchwr,n) > Te,min

INF Te(Tchwr,n) ≤ Te,min

. (8)

Predictive pre-cooling controloptimization method

In the previous section, an objective functionwas defined for the pre-cooling control algorithm,420which contains penalties for energy consumed bythe cooling system, operative temperatures outsideof a defined comfort region, and low evaporatingtemperatures. This section describes how the objec-tive function in Equation 1 is minimized to optimize425the chiller control over a 24-h look-ahead schedule.

Each hourly cost component of the objectivefunction is evaluated sequentially from hour 1 to24. At a given timestep, the choice of compressorspeed will determine the cooling rates QCchiller and430Pchiller and, along with exogenous variable forecasts,will determine To, Tcc, Tchwr, and Te at the next time

step. The power consumption and cooling rate of thechiller are non-linear functions of Tx, Te, ω, and f ,where Te depends on previous choices of compres- 435sor speed.

The chiller capacity and power consumption arediscontinuous at the minimal compressor speed, atwhich they drop to zero. This discontinuity in powerconsumption, representing the finite minimum ca- 440pacity of the chiller and its auxiliary equipment,precludes the use of gradient-based optimizationmethods. Optimization methods that do not requirecalculation of a gradient, such as direct search, gen-eralized pattern search (GPS), genetic algorithms, 445and simulated annealing, were considered for appli-cation to this problem. In practice, GPS (Torczon1997; Lewis and Torczon 1999, 2000, Audit andDennis 2003) was found to identify near-optimalsolutions within a few minutes on a standard per- 450sonal computer.

The GPS seeks optimal compressor speeds forevery timestep N in the 24-h-ahead schedule ofchiller operation, resulting in a 24-dimensionalsearch space. The compressor speed at each hour 455can take the values of ω = 0 Hz (off), and anywherewithin its range of operation, ωmin < ω < ωmax andthe resulting sequence of current and past ωN deter-mine the evolution of Pchiller, QCchiller, To, Te, andTcc at the next timestep. Beginning with a guess at an 460initial point in the 24-dimensional grid of compres-sor speeds, the GPS evaluates, or polls, the objectivefunction at a grid of points created with a given gridstep size surrounding the initial guess for a moreoptimal solution. If a more optimal solution is iden- 465tified, the grid is polled again around that new point.The grid step size is increased, up to the maximumstep size; each time a more optimal point in the gridis identified to ensure that basins of convergence farfrom the current point are tested. If a more optimal 470grid point is not found at the largest grid step size,the GPS continues around the current point with asmaller grid step size, down to a minimum step sizeto find the most optimal solution in that region ofconvergence. The GPS stops when no more optimal 475points can be found at the smallest grid step size.

A detailed explanation of the GPS algorithmis included Matlab’s Global Optimization Toolbox:User’s Guide (Mathworks 2010), and more infor-mation can be found in Torczon (1997), Lewis and 480Torczon (1999, 2000), and Audit and Dennis (2003).Unlike the gradient-based method, GPS can searchdifferent basins of convergence from an initial guesswithin, for example, a basin of a local optimum.


HVAC&R RESEARCH 7

Figure 1. Closed-loop optimization of compressor speed for low-lift cooling of the TABS with pattern search.

However, GPS does not guarantee convergence to a485global optimum.

A flowchart of the GPS algorithm implementedfor optimizing the daily schedule of compressorspeeds is shown in Figure 1. An initial guess of 24compressor speeds

⇀ωn is made at each hour, which490

may be based on the previous hour’s result. TheGPS algorithm is run to identify an optimal sched-ule of compressor speeds

⇀ωopt for the next 24 hours.

At each iteration of the pattern search, Equations 2through 8 are applied to calculate Pchiller, QCchiller,495To, Tcc, and Tchwr. The pattern search may be re-peated at each hour in a closed-loop optimization(Henze et al. 2004) with updated forecasts of out-door air temperature Tx, adjacent zone air temper-ature Ta, and internal loads QI at each hour. The500optimal compressor speed for the first hour of theoptimization, computed by the GPS, determinesthe chiller compressor speed for the next hour, afterwhich the process is repeated.

Experimental implementation of505

low-lift predictive pre-cooling ofTABS

The predictive control algorithm described abovehas been implemented on a low-lift chiller serving aconcrete-core TABS in an experimental test cham-510ber. The primary objective of these experiments wasto experimentally test the effectiveness of the pre-dictive pre-cooling control algorithm. A secondaryobjective was to compare the sensible cooling en-ergy performance of the pre-cooled TABS radiant515

cooling system with a case similar to one of thesimulated basecase systems studied by Katipamulaet al. (2010). Of the eight other cases simulated,the radiant system with a variable-capacity chilleris closest to the variable-capacity, split-system air 520conditioner used as the experimental base case. Thesimulation and experimental base cases are simi-lar in the lack of pre-cooling TES, transport energycosts, and chiller performance.

Experimental facilities 525

An existing experimental test facility (Yang1999; Kobayashi 2001) was adapted for testing low-lift cooling experimentally. The lab includes twochambers, one test chamber representing a typicaloffice zone with one exterior wall and another cli- 530mate chamber used to simulate climate conditionsoutside the exterior wall. The test chamber has di-mensions of roughly 3.66 m by 5.18 m by 2.44 m(12 ft by 17 ft by 8 ft). The walls of both chambersare heavily insulated with a thermal resistance of 535about 5.3 m2-K/W (30 ft2-F-hr/BTU). A partitionwall separates the test and climate chambers, whichcontains three large double-pane windows with athermal resistance of approximately 0.27 m2-K/W(1.53 ft2-F-hr/BTU). The surrounding environment 540is a 6 m by 12 m (20 ft by 40 ft) high-bay laboratoryspace maintained at 20◦C to 24◦C (68◦F to 75.2◦F).

The climate chamber temperature is controlledby a constant-volume air handling unit with thereturn air temperature set-point adjusted at every 545hour to follow the typical summer week of a typicalmeteorological year (TMY) weather file. The test



Figure 2. Schematic elevation of the experimental test chamber and cooling system.

chamber has a modular floor constructed to mimic aTABS using an aluminum-faced subfloor, polyethy-lene (PEX) pipe, and 14.6-cm (5.75-in.) concrete550pavers. Chilled water supplied by the low-lift chillercools the bottom of the concrete pavers via thealuminum-faced subfloor, resulting in a thermal lagbetween the time cooling is delivered and heat isabsorbed from the test chamber. The air-cooled555variable-capacity low-lift chiller is installed in theclimate chamber. The chiller was constructed usingan off-the-shelf variable capacity split-system airconditioner condensing unit, described in Gayeskiet al. (2010), with a rated seasonal energy efficiency560ratio (SEER) of 16 BTU/Wh (4.69 Wth/We). Toconvert this condensing unit to a low-lift chiller, a re-frigerant loop through a brazed-plate heat exchanger(BPHX) was added along with means to control thecompressor at low speeds to enable low-lift oper-565ation. A schematic of the variable capacity chiller,the climate and office test chamber, and associatedinstrumentation is shown in Figure 2. Lighting andelectrical resistance heating elements simulate typ-ical office internal gains.570

There are six parallel water loops in the radiantfloor, each made of 12.7-mm (0.5-in.) PEX pipe,

designed to minimize pressure drop in the TABSradiant floor. The pipe spacing of 30.5 cm (12 in.) islarge and results in unnecessarily low chilled-water 575temperatures and will be modified in future work.The chilled-water pump serving the radiant floorwas operated at a constant speed of 0.13 L/s (2.1GPM) with a power consumption of approximately145 W/L/s (9.1 W/GPM). A variable-speed pump 580may further improve the low-lift cooling system ef-ficiency but will also increase model and optimiza-tion complexity.

Data-driven temperature response modelidentification 585

The coefficients of Equations 4 through 6, whichpredict zone operative temperature, TABS concrete-core temperature, and chiller evaporating tempera-ture, must be identified from monitored data. In thecase of the experimental test chamber, the temper- 590atures and loads in Equations 4 through 6 refer tomeasured variables from sensors installed in the of-fice test chamber and climate chamber shown inFigure 3. To, Tx, Ta, and Tcc are calculated from sur- Q2

face and air temperatures measured using 24-gauge 595


HVAC&R RESEARCH 9

Figure 3. Left: Test office chamber with concrete radiant floor, simulated internal loads, and a conventional split-system air conditionerindoor unit. Right: Low-lift chiller in the climate chamber.

special-limits thermocouples. Thermocouples con-nected to a given multiplexer agree with each otherto within 0.01 K (0.018◦F) + 0.4%. Terminal ref-erence sensors are accurate to 0.4 K over −25◦C to50◦C (0.7◦F over −13◦F to 122◦F) and have been600found to agree within 0.1 K (0.2 F) at room temper-ature. QI , the internal heat rate to the zone, is mea-sured using Wattnode power meters with a ratedaccuracy of 0.5%. The cooling rate delivered bythe chiller, QCchiller, is calculated from chilled-water605flow rate measured with an Omega FTB8007B flowmeter with an acurracy of 1.5%, and supply andreturn temperatures, Tchws and Tchwr, are measured

using special-limits 1/16′′ sheathed thermocoupleprobes. 610

M th-order models of zone operative tempera-ture and concrete-core temperature can be iden-tified from at least 4 days of training data usingmulti-variable regression. A specific sample train-ing dataset used to estimate the parameters of the 615models given by Equations 4 through 6 for theexperimental test chamber is shown in Figure 4.For this test chamber, an eighth-order model, with30-min sampling intervals, provided the best 24-h-ahead prediction accuracy (Gayeski 2010) when ap- 620plied to separate validation datasets. For a variety of

Figure 4. Sample temperature response model training data.



Figure 5. Twenty-four-hour-ahead forecasts relative to measured data of test chamber operative temperature (left) and concrete coreand return water temperature (right).

validation datasets spanning different cooling rates,internal load schedules, and climate conditions, op-erative temperature and concrete-core temperaturecould be predicted over a 24-h look ahead with625root-mean-square error (RMSE) of less than 0.5◦C(0.9◦F) (Gayeski 2010). A second-order model forchilled-water return temperature was identified fromthe same training data based on measured coolingrates and the TABS concrete-core temperature Tcc.630This model had an RMSE of less than 1◦C (1.8◦F)across all validation datasets. From the predictionof chilled-water return temperature, the evaporatingtemperature at the chiller can be calculated using theapproach temperature of the BPHX. The 24-h-ahead635forecasts of operative temperature, concrete-coretemperature, and chilled-water return temperatureare compared to measured values in Figure 5. Usinglogged data from a building automation system, thecoefficients of these models could be updated con-640tinuously in a full-scale building. The model orderand sampling intervals that lead to the most accuratedata-driven temperature response models will differfor different buildings and can be selected based onvalidation data prediction accuracy.645

Experimental test procedure

The sensible cooling performance of the low-lift cooling system with predictive pre-cooling of aTABS was compared to that of a variable-capacitysplit-system air conditioner in the test chamber. The650split system represents one of the cases with no

pre-cooling simulated by Katipamula et al. (2010).Two pairs of experiments were performed wherethese systems were subjected to the typical sum-mer week of the TMY weather for Atlanta, GA, at 655Hartsfield-Jackson airport and for Phoenix, AZ, atDeer Valley airport, August 24–30 in both cases.The internal heat rate for the Atlanta tests repre-sented standard performance loads for lighting andinternal equipment gains (Gayeski 2010; Katipa- 660mula et al. 2010), but high occupant loads, for atotal of 36.6 W/m2 (11.6 BTU/hr-ft2) at peak loadand a load schedule representative of a small com-mercial office. High-performance loads for lightingand internal equipment gains (Gayeski 2010; Kati- 665pamula et al. 2010), but again high occupant loads,were applied in the Phoenix tests at a heat rate of21.5 W/m2 (6.8 BTU/hr-ft2) at peak load. The totalloads, including high occupant loads, are oversizedto better match the chiller capacity and allow a suit- 670able range for chiller operation.These loads weremeasured with electric power meters as denoted inFigure 2. The adjacent zone temperature Ta in Equa-tions 4 and 5, represents the external lab temperatureand was held nearly constant. In these experiments 675QI , Tx, and Ta are controlled and are thus predictableinputs to the models and optimization algorithms. Inpractice, these variables will have error and uncer-tainty in prediction that must be taken into account(Henze and Krarti 1999). 680

Because the TABS radiant floor provides onlysensible cooling, the relative humidity of the cham-ber was kept as low as possible to avoid latent


HVAC&R RESEARCH 11

Figure 6. Typical low-lift pre-cooling optimization for TABS from an experimental test chamber (color figure available online).

cooling. As discussed above, latent cooling wouldbe performed separately by a DOAS with separate685DX cooling or efficient dehumidification. Any con-densed water produced during testing with the con-ventional indoor unit was collected and weighed inorder to adjust cooling energy to the sensible coolingbasis.690

The following process was employed to evalu-ate energy and thermal performance of the low-lift cooling system relative to the variable-capacitysplit-system air conditioner.

� The climate chamber was controlled at each hour695to achieve typical summer week temperatures.

� The internal loads were controlled to deliver theload schedules defined above to the test chamber.

� The low-lift cooling system with the TABS wasoperated for one week, including one week-700end, maintaining operative temperature between19.5◦C and 25◦C (67◦F and 78◦F) (ASHRAE2007) while occupied.

� The variable-capacity split-system air conditionerwas operated for one week, including one week-705end, using conventional thermostatic control to

achieve the same daily average temperature asthe low-lift cooling system.

Figure 6 illustrates a typical sequence of optimalcompressor speeds for a 24-h look-ahead schedule 710produced by the predictive control algorithm. Com-pressor speeds for each of the 24 hours into thefuture are shown at the top left. The predicted op-erative temperature To, concrete-core temperatureTcc, return water temperature Tchwr, and evaporating 715temperature Te for this schedule are shown at thetop right. The predicted chiller power consumptionPchiller is shown at bottom left, and the cumulativeenergy consumption is shown at bottom right. Forthe hour following this optimization, the low-lift 720chiller would be operated at the first predicted opti-mal compressor speed, which is 0 Hz (or off) in thecase below, and the predictive control optimizationwould be repeated at the next hour, with the previoushour’s schedule as an initial guess for the GPS. 725

The sequences illustrated in Figure 6 demon-strate certain aspects of predictive control for low-lift cooling with TABS. First, the most efficient timeto perform most of the cooling is at night and during



Figure 7. Operative temperature and system power consumption of the low-lift cooling system and a split-system air conditionersubject to Atlanta typical summer week conditions and standard efficiency loads.

the early morning hours under low-lift conditions.730Second, the chiller runs at low part loads more of thetime, which is also more efficient. Third, becausethe efficiency of the chiller depends on evaporat-ing temperature, the compressor cycles off at timesto avoid low evaporating temperatures and provide735higher chiller efficiency while operating at the lowend of its capacity range.

This predictive control algorithm operates con-tinuously during the course of each experiment,updating chiller compressor speed, fan speed, and740chilled-water pump availability at each hour. Thetests described here were used to measure the perfor-mance of the algorithm under typical summer weekconditions. The data-driven temperature responsemodel is not guaranteed to be valid under operat-745ing conditions not previously observed. However,the model may be updated continuously and overtime be trained for a broad range of thermal inputs.Non-ideal cases, such as rapid or high frequencychanges in internal gains, were not tested experi-750mentally. These types of inputs, if the models hadnot yet been trained for them, would lead to greatererror in model predictions.

Energy and thermal performance

Figure 7 shows the zone operative temperature755response and the system power consumption for theAtlanta test for the low-lift cooling system and thevariable-capacity split-system air conditioner span-

ning the week of occupied operation. The intial tem-perature conditions for the split- and low-lift sys- 760tems differ because the systems were tested understeady-periodic behavior, operating under the samesystem for the previous week and achieving a typi-cal Monday start-up condition. Two characteristicsof low-lift cooling are apparent in the pattern of en- 765ergy consumption: (1) the cooling rate is distributedover time, allowing the chiller to run at lower speedsand lower part loads and (2) cooling is delivered tothe TABS overnight when lower condensing tem-peratures are possible. 770

The average daily mean operative temperaturedifference between the low-lift cooling system testsand the split-system tests was small: 0.3◦C (0.54◦F)for the Atlanta tests and −0.5◦C (−0.9◦F) for thePhoenix tests. The main difference in thermal envi- 775ronment provided by the systems is the slow tem-perature rise of the conditioned zone for low-liftcooling with TABS. High convective internal loadscause significant increases in zone operative tem-perature relative to the radiant cooling surface. This 780is a recognized limitation of radiant cooling systemand TABSs (Meierhans 1996; Koschenz and Dorer1999), which may preclude the application of theTABS, and low-lift cooling with a TABS, from lowperformance buildings and buildings with high in- 785ternal loads.

Results similar to those shown in Figure 7 wereobserved for testing under Phoenix conditions withhigh-performance internal loads, except that the


HVAC&R RESEARCH 13

Table 1. Energy performance (±0.5%) of low-lift cooling system relative to a variable-capacity split-system air conditioner.

Atlanta, August 24–30 Phoenix, August 24–30

Performance metric Split system Low lift Difference Split system Low lift Difference

Energy (Whe) 14,465 10,982 −25% 21,153 17,205 −19%Energy consumption

(Whe)a with latentcooling deducted

14,053 10,982 −22% 21,153 17,205 −19%

Average pressure ratio 1.91 1.70 −11% 2.12 1.99 −6%

aThe latent energy consumption can only be estimated for split-system operation based on measurement of the mass of watercondensed. The low-lift system also may have performed some latent cooling.

lower internal convective heat rate provided by high-790performance internal loads led, despite higher out-door temperatures, to a lower operative temperaturerise. A comparison of the energy performance ofthe two cooling systems in each of the two climatesis shown in Table 1. The table shows relative per-795formance in terms of energy consumed and averagepressure ratio, representative of internal lift, for theperiod of each test.

The results show that low-lift cooling systemsensible cooling energy savings can be signifi-800cant relative to a high-efficiency split-system airconditioner that uses the same variable-capacitycompressor-condensing unit but with no pre-cooling. The measured energy consumption of theexperimental low-lift cooling system was 25% less805than the split-system under Atlanta conditions and19% less under Phoenix conditions. Accounting,conservatively, for latent cooling performed bythe split system reduces the savings to 22% forAtlanta.810

The closest system configurations modeled byKatipamula et al. (2010) are not directly compara-ble to the systems tested experimentally; however,it is of interest to evaluate the experimental savingsrelative to the most similar simulated savings for the815same typical summer weeks in Atlanta and Phoenixand similar internal loads. The simulated case witha variable-speed chiller and radiant distribution isclosest to the variable-capacity split system testedexperimentally due to its low fan power and the820absence of latent cooling. The simulated sensiblecooling energy savings of a low-lift cooling sys-tem with variable-speed chiller and pre-cooling ofideal TES relative to a comparable system withoutpre-cooling TES in Atlanta was 26%. For Phoenix,825with high-performance loads, the simulated sensiblecooling energy savings for the typical summer week

were 29%. Differences between simulated savingsand experimental savings are expected for severalreasons; the simulated TABS included an ideal TES 830(pre-cooling case), a different chiller performancemap, and a lower (per unit capacity) evaporator tozone thermal resistance. Neither the experimentalnor the simulated savings presented above includethe additional total cooling savings relative to con- 835ventional VAV systems, which could be providedby efficient latent cooling by a DOAS (Katipamulaet al. 2010).

The limitations of the experimental facilityshould be considered when interpreting the experi- 840mental results. The following factors caused lowerlow-lift cooling system performance than possiblein theory:

� an oversized chiller is used because a compressorsmall enough to match the small test chamber was 845not available;

� the internal loads are oversized to better matchthe chiller capacity but cause a greater operativetemperature rise and zone temperatures closelycoupled to convective loads and, consequently, 850less load shifting;

� the single-story test chamber allows cooling en-ergy losses through the floor, which would notoccur in a multi-story building; and

� the chilled-water pipe spacing of 30 cm (12 in.) 855dictated by standard radiant heating componentsis, in retrospect, too large for low-lift coolingapplications with a TABS. A smaller pitch of10–15 cm (4–6 in.) typical of radiant coolingapplications would result in higher chilled-water 860temperatures, higher evaporating temperatures,and more efficient low-lift cooling system opera-tion.



Although the results presented above reflect acomparison of a low-lift cooling system with only865one other system configuration in two climates,they are a useful benchmark against simulationsconducted in previous research (Armstrong et al.2009a, 2009b; Katipamula et al. 2010). Further re-search is required to adapt and implement this con-870trol scheme in alternative low-lift cooling systemconfigurations, incorporate DOAS control of latentloads, and compare performance to other systems.These comparisons, however, are not trivial. Build-ing simulation tools are still not fully capable of sim-875ulating receding horizon model-predictive controlalgorithms that include deteailed models of coolingsystem performance and building temperature re-sponse, especially with thermally massive TABSs.Experimental comparisons are possible but expen-880sive, time-consuming, and subject to uncertaintiesthat simulations neglect.

Summary

This article presents a data-driven, model-based predictive control algorithm for low-lift885chillers serving a concrete-core TABS and itsimplementation in an experimental test cham-ber. Temperature- and load-dependent curve-fitchiller performance models and zone operativetemperature and concrete-core temperature CRTF890models are incorporated into a predictive controloptimization algorithm. The algorithm determinesoptimal sequences of compressor and condenserfan speeds for each 24-h period to minimize low-liftcooling system energy consumption while main-895taining thermal comfort. Closed-loop optimizationhas been successfully implemented in which theoptimal chiller control schedule is determinedat every hour based on the latest measured zonetemperatures and internal loads. In practice, these900hourly updates would also consider new forecastsof weather and internal loads.

An experimental implementation of the predic-tive control algorithm for low-lift cooling with aTABS demonstrated significant sensible cooling905energy savings, consistent with previous simulationresults for low-lift cooling systems (Katipamulaet al. 2010). The experimental base system wasa high-efficiency split system served by the sameoutdoor unit (compressor, condenser, EXV, andQ3910power electronics) employed by the low-lift chiller.The experiments for low-lift cooling with theTABS under Atlanta conditions with standard

performance loads showed 25% sensible coolingenergy savings, and under Phoenix summer 915conditions with high-performance internal loads,it was 19%. Latent cooling energy has not beenincluded because low-lift cooling systems utilize aseparate DOAS for dehumidification, as describedby Armstrong et al. (2009a, 2009b). 920

Discussion

The predictive control strategy presented herehas been developed primarily for single-zonelow-lift predictive pre-cooling of TABS withpredictable loads. A number of important additions 925and revisions must be made to this control strategyfor implementation in a broader context. First,the algorithm should be revised to include solarloads, measured or estimated occupant behaviors,and multi-zone control and supply of the TABS. 930The inclusion of a variable-speed chilled-waterpump serving the TABS and the chiller will also beimportant, as it may allow for further improvementsin chiller efficiency and control.

A strategy that combines pre-cooling of TABS 935with direct cooling of zones is likely to achieve thebest balance of system efficiency and comfort con-trol. This will also ameliorate the effects of errors inforecasts of exogenous variables, such as internal orsolar gains and outdoor temperature, and errors in 940predictions of zone temperatures by the data-drivenmodels. Therefore, another important advance willbe to incorporate the option for direct cooling of thezone, not through TABS but through conventionalair-side evaporators, large heat exchanger fan coil 945units, or radiant cooling panels.

It is worth reiterating that the objective in thiswork has been to minimize the energy needed to runthe cooling system. To minimize cost, one needs tomodify the rate function rN in Equation 1 using real- 950time or time-of-use rates and add demand charges tothe objective function. Experience has shown con-sistently that the resulting percent savings in oper-ating cost are substantially larger than the energysavings percentages (minimum based on flat rate) 955for most utility customers.

Improvements to the experimental implementa-tion of low-lift cooling with TABS presented in thisarticle will improve both the energy and thermalperformance of the system. These improvements in- 960clude better load matching, decreased chilled-waterpipe pitch, additional under-slab insulation to better


HVAC&R RESEARCH 15

mimic multi-story performance, and optimizationof the BPHX.

Acknowledgments965

The authors wish to acknowledge the Masdar In-stitute of Science and Technology for support of thisresearch. They are grateful for the support and ad-vice of members of the Mitsubishi Electric ResearchLaboratory and the Pacific Northwest National Lab-970oratory. Nicholas Gayeski is also thankful for thesupport of the Martin Family Society of Fellowsfor Sustainability. Thanks to Heejin Cho and Pa-cific Northwest National Laboratory for providingthe intermediate files needed to conduct the mod-975eled savings comparison and to Kurt Keville and theMIT Solar Decathlon team for the split system.

ReferencesAdlam, T. 1948. Radiant Heating. New York, NY: The Industrial

Press.

Q4

980Armstrong, P.R., W. Jiang, D. Winiarski, S. Katipamula, and

L.K. Norford. 2009a. Efficient low-lift cooling with radi-ant distribution, thermal storage, and variable-speed chillercontrols—Part II: Annual energy use and savings. HVAC&RResearch 15(2):402–32.985

Armstrong, P.R., W. Jiang, D. Winiarski, S. Katipamula, L.K.Norford, and R.A. Willingham. 2009b. Efficient low-lift cool-ing with radiant distribution, thermal storage, and variable-speed chiller controls—Part I: Component and subsystemmodels. HVAC&R Research 15(2):366–400.990

Armstrong, P.R., S.B. Leeb, and L.K. Norford. 2006a. Con-trol with building mass—Part I: Thermal response model.ASHRAE Transactions 112(1).Q5

Armstrong, P.R., S.B. Leeb, and L.K. Norford, 2006b. Controlwith building mass—Part II: Simulation. ASHRAE Transac-995tions Vol. 112(1).Q6

ASHRAE. 2007. ANSI/ASHRAE Standard 55-2007, thermalenvironmental conditions for human occupancy. Atlanta,GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.1000

Audit, C., and J.E. Dennis. 2003. Analysis of generalized patternsearches. SIAM Journal on Optimization 13(3):889–903.

Brandemuehl, M.J., M.J. Lepoer, and J.F. Kreider. 1990. Model-ing and testing the interaction of conditioned air with buildingthermal mass. ASHRAE Transactions 96(2):871–5.1005

Braun, J.E. 1990. Reducing energy costs and peak electrical de-mand through optimal control of building thermal storage.ASHRAE Transactions 96(2):876–88.

Braun, J.E. 2007. Impact of Control on Operating Costs for CoolStorage Systems with Dynamic Electric Rates. ASHRAE1010Transactions 113(2): 343–354.

Braun, J.E., and N. Chaturvedi. 2002. An inverse gray-box modelfor transient building load prediction. HVAC&R Research8(1):73–97.

Braun, J.E., and K.H. Lee. 2006. Assessment of demand limiting 1015using building thermal mass in small commercial buildings.ASHRAE Transactions 112(1):547–58.

Brunello, P., M.D. Carli, M. Tonon, and R. Zecchin. 2003. Ap-plications of heating and cooling thermal slabs for differ-ent buildings and climates. ASHRAE Transactions Symposia, 10202003:637–46.

Conniff, J.P. 1991. Strategies for reducing peak air-conditioningloads by using heat storage in the building structure. ASHRAETransactions 97(1):704–9.

Doebbler, I.M., M. Moore, and M. Deru. 2010. Radiant slab 1025cooling for retail. ASHRAE Journal 52(12):28–39.

Eto, J.H. 1984. Cooling strategies based on indicators of thermalstorage in commercial building mass. Annual Symposium onImproving Building Energy Efficiency, College Station, TX,August 14–15. 1030

Gayeski, N. 2010. Predictive pre-cooling control for low-lift ra-diant cooling using building thermal mass. Doctoral disser-tation, Massachusetts Institute of Technology, Cambridge,MA.

Gayeski, N., T. Zakula, P.R. Armstrong, and L.K. Norford. 2010. 1035Empirical modeling of a rolling-piston compressor heat pumpfor predictive control in low-lift cooling. ASHRAE Transac-tions 117(2):ML-11-023. Q7

Guntensperger, W., M. Gwerder, A. Haas, B. Lehmann, F. Reng-gli, and J. Todtli. 2005. Control of concrete core conditioning 1040systems. 8th REHVA World Congress for Building Technolo-gies(CLIMA 2005), Lausanne, Switzerland, October 10–12.

Gwerder, M., J. Todtli, B. Lehmann, V. Dorer, W. Guntensperger,and F. Renggli. 2009. Control of thermally activated buildingsystems (TABS) in intermittent operation with pulse width 1045modulation. Applied Energy 86:1606–16.

Henze, G., R.H. Dodier, and M. Krarti. 1997. Developmentof a predictive optimal controller for thermal energy stor-age systems. International Journal of HVAC&R Research3(3):233–64. 1050

Henze, G., C. Felsmann, and A. Florita. 2008. Optimization ofbuilding thermal mass control in the presence of energy anddemand charges. ASHRAE Transactions 114(2):75–84.

Henze, G., C. Felsmann, and G. Knabe. 2004. Evaluation of op-timal control for active and passive building thermal storage. 1055International Journal of Thermal Sciences 43(2):173–83.

Henze, G., A. Florita, M. Brandemuehl, C. Felsmann, and H.Cheng. 2010. Advances in near-optimal control of passivebuilding thermal storage. Journal of Solar Energy Engineer-ing 132:021009. 1060

Henze, G., and M. Krarti. 1999. The impact of forecasting un-certainty on performance of a predictive optimal controllerfor thermal energy storage systems. ASHRAE Transactions105(1):553–61.

Henze, G.P. 2003. Impact of real-time pricing rate uncertainty on 1065the annual performance of cool storage systems. Energy andBuildings 35:313–25. Q8

Henze, G.P., J. Pfafferott, S. Herkel, and C. Felsmann. 2007.Impact of adaptive comfort criteria and heat waves on op-timal building thermal mass control. Energy and Buildings 107039:221–35.

Jiang, W., D.W. Winiarski, S. Katipamula, and P.R. Arm-strong. 2007. Cost-effective integration of efficient low-lift



base-load cooling equipment. PNNL-17157. Pacific North-west National Laboratory. Richland, WA.1075

Katipamula, S.K., P.R. Armstrong, W. Wang, N. Fernandez,H. Cho, W. Goetzler, J. Burgos, R. Radhakrishnan, and C.Ahlfeldt. 2010. Cost-effective integration of efficient low-liftbaseload cooling equipment FY08 final report. PNNL-19114.Pacific Northwest National Laboratory. Richland, WA.1080

Keeney, K., and J.E. Braun. 1996. A simplified method for deter-mining optimal cooling control strategies for thermal storagein building mass. International Journal of HVAC&R Research2(1):59–78.

Kintner-Meyer, M., and A.F. Emery. 1995. Optimal control of1085an HVAC system using cold storage and building thermalcapacitance. Energy and Buildings 23:19–31.

Kobayashi, N. 2001. Floor-supply displacement ventilation sys-tem. Master’s thesis, Massachusetts Institute of Technology,Cambridge, MA.1090

Koschenz, M., and V. Dorer. 1999. Interaction of an air sys-tem with concrete core conditioning. Energy and Buildings30:139–45.

Krarti, M., G. Henze, and D. Bell. 1999. Planning horizon fora predictive optimal controller for thermal energy storage1095systems. ASHRAE Transactions 105(2).Q9

Lehmann, B., V. Dorer, and M. Koschenz. 2007. Applicationrange of thermally activated buildings systems tabs. Energyand Buildings 39(2007):593–8.

Lewis, R.M., and V. Torczon. 1999. Pattern search algorithms1100for bound constrained minimization. SIAM Journal on Opti-mization 9(4):1082–99.

Lewis, R.M., and V. Torczon. 2000. pattern search methods forlinearly constrained minimization. SIAM Journal on Opti-mization 10(3):917–41.1105

Liu, M., and D.E. Claridge. 1998. Use of calibrated HVAC sys-tem models to optimize system operation. Journal of SolarEnergy Engineering 120.Q10

MathWorks. 2010. Matlab Global Optimization Toolbox 3:User’s Guide. Natick, MA.

Q11

1110Meierhans, R.A. 1996. Room air-conditioning by means of

overnight cooling of the concrete ceiling. ASHRAE Trans-actions 102(1):693–7.

Mumma, S., and K. Shank. 2001. Achieving dry outside air inan energy-efficient manner. ASHRAE Transactions 107(1): 11151–9.

NIST. 2009. NIST reference fluid thermodynamic and transportproperties database (REFPROP): Version 8.0. NIST standardreference database 23. Q12

Olesen, B.W., K. Sommer, and B. Duchting. 2002. Con- 1120trol of slab heating and cooling systems studied by dy-namic computer simulations. ASHRAE Transactions 108(2):698–707.

Rabl, A., and L.K. Norford. 1991. Peak load reduction by precon-ditioning buildings at night. International Journal of Energy 1125Reseach 15:781–98.

Roth, K., J. Dieckmann, and J. Brodrick. 2009. Usingoff-peak precooling. ASHRAE Journal March 2009. http://findarticles.com/p/articles/mi m5PRB/is 3 51/ai n32067827/. Q13

Seem, J.E. 1987 Modeling of heat transfer in buildings. PhD 1130thesis, University of Wisconsin, Madison, WI.

Snyder, M.E., and T.A. Newell. 1990. Cooling cost minimizationusing building mass for thermal storage. ASHRAE Transac-tions Research SL-90-14-3. Q14

Stephenson, D.G., and G.P. Mitalas. 1967. Cooling load calcula- 1135tions by thermal response factor method. ASHRAE Transac-tions 73(1). Q15

Stephenson, D.G., and G.P. Mitalas. 1971. Calculation of heatconduction transfer functions for multi-layer slabs. ASHRAETransactions 77(2):117–26. 1140

Torczon, V. 1997. On the convergence of pattern search algo-rithms. SIAM Journal on Optimization 7(1):1–25.

Yang, X. 1999. Study of building material emissions and indoorair quality. PhD thesis. Massachusetts Institute of Technol-ogy, Cambridge, MA. 1145

Zakula, T. 2010. Heat pump simulation model and optimalvariable-speed control for a wide range of cooling condi-tions. Master’s thesis. Massachusetts Institute of Technology,Cambridge, MA.

Zakula, T., N.T. Gayeski, P.R. Armstrong, and L.K. Norford. 11502011. Variable-speed heat pump model for a wide rangeof cooling conditions and loads. International Journal ofHVAC&R Research 17(5):670–91. Q16

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