a novel and dynamic demand-controlled ventilation strategy for co2 control and energy saving in...

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Energy and Buildings 43 (2011) 2499–2508

Contents lists available at ScienceDirect

Energy and Buildings

j ourna l ho me p age: www.elsev ier .com/ locate /enbui ld

novel and dynamic demand-controlled ventilation strategy for CO2

ontrol and energy saving in buildings

ao Lu ∗, Xiaoshu Lü, Martti Viljanenepartment of Civil and Structural Engineering, School of Engineering, Aalto University, Rakentajanaukio 4 A, Otaniemi, Espoo, Finland

r t i c l e i n f o

rticle history:eceived 8 October 2010eceived in revised form 1 June 2011ccepted 3 June 2011

eywords:emand-controlled ventilationnergy savingarbon dioxide

a b s t r a c t

Although conventional CO2-based demand-controlled ventilation strategies, such as proportional andexponential controls, can ensure buildings/spaces meeting the minimum requirements of outdoor air byindustry standards, they are operated under the assumption of equilibrium condition which can hardlybe reached in practice and therefore there is still much space to improve on conventional strategies interms of energy saving. In this paper, a novel and dynamic control strategy was developed for hourlyscheduled buildings. The strategy utilized schedules by setting a base ventilation rate for unoccupiedperiods and calculating ventilation rate dynamically at each occupied period by solving the CO2 massbalance equation to keep indoor CO2 near the set point during the occupied period. Experimental simu-

arbon dioxide mass balance equationroportional control

lations were made over a sports training center using both simulated and experimental CO2 generationrates. Results show that the new strategy can save +34% of energy related to ventilation air comparedto proportional control. The new strategy was also extended to common buildings which are occupiedfor almost all opening hours. In the case of common buildings, the new strategy can save about +26% ofenergy related to ventilation air compared to proportional control. The new strategy is simple, dynamic,flexible and efficient.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

Buildings are becoming one of the fastest growing energy con-uming sectors. In European Union (EU), 40–50% of total energyonsumption comes from buildings [1]. In addition, there is alsoncrease demand for thermal comfort and indoor air quality (IAQ).herefore, efforts have been much focused on energy efficientuildings which can provide comfort indoor environment with theinimum possible energy cost. Consequently, the role of control

ystems is becoming significant since they are directly related to themount of energy consumed in the buildings and comfort of occu-ants. Various strategies, ranging from simple to complex, haveeen proposed to reduce energy consumption. One of the most

mportant strategies is carbon dioxide (CO2) monitoring. In thistrategy, calculations are used to relate indoor CO2 level to the freshutside air, in m3/s per person, being provided to a space because
he CO2 level is generally used as an indicator of the number of occu-ants and CO2 itself is not considered as a dangerous contaminant.any techniques were developed for CO2 monitoring. CO2-based
∗ Corresponding author. Tel.: +358 09 47025306; fax: +358 09 3512724.E-mail addresses: [email protected], [email protected] (T. Lu).

378-7788/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.enbuild.2011.06.005

demand-controlled ventilation (DCV) is probably the best knownone [2–7]. Literatures show that CO2 can be used to detect occu-pancy for DCV systems as it is an excellent surrogate gas for theconcentrations of occupant-related contaminants [5,8,9]. By usingthe dynamic CO2 detection method, the change of occupancy canbe detected and determined with a fast response time [10] and,in turn, the outdoor air supply rate per person recommended inthe industrial standards ASHRAE 62-2007 [11] can be met. Differ-ent publications also have demonstrated that CO2-based DCV hadled to the reduction of energy consumption. Congradac and Kulic[12] used genetic algorithms to optimize the return damper posi-tion such that indoor CO2 concentration can be kept close to thedesired level as possible and at the same time the lowest valueof the valve (the lowest energetic use) can be accomplished. Theirmethod was verified by the simulation. Pavlovas [13] provided acase study over a Swedish multifamily apartment aiming at eval-uating the demand-controlled ventilation system with differentstrategies. The outcome of the simulation shows that it would bepossible to achieve energy savings using occupancy and/or humid-
ity controlled ventilation to reduce the average ventilation flowrate while keeping an acceptable indoor climate. Mysen et al.[14] inspected 157 Norwegian classrooms to analyze the energyuse over there different ventilation systems: Constant Air Volume
dx.doi.org/10.1016/j.enbuild.2011.06.005

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dx.doi.org/10.1016/j.enbuild.2011.06.005


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CAV), CO2 sensor based demand-controlled system (DCV-CO2) andnfrared occupancy sensor based demand-controlled system (DCV-R). Their results show that DCV-CO2 and DCV-IR reduce the energyse due to ventilation in the average classroom to 38% and 51%,espectively, compared to the corresponding energy for a CAV sys-em.

Control algorithms for CO2-based DCV have been a hot topic for long time [4,5,10,15,16], among which proportional and expo-ential controls are the most discussed and popular ones. Bothpproaches modulate ventilation between a lower set point ofndoor CO2 and an upper set point that represents the equilib-ium concentration of CO2 corresponding to the target per-personentilation rate of a space [5], the difference between two liesn the modulation of ventilation rate which is proportionally forroportional control but exponentially for exponential control.xponential control is able to adjust ventilation rate more quicklyo changes in CO2 concentration by using a standard proportional-lus-integral (PI) or proportional-integral-derivative (PID) controllgorithm. From the energy conservation point of view, exponen-ial control adds little additional benefit compared to proportionalontrol generally [17]. However the potential energy saving foroth is affected by the factor how closely the actual design ventila-ion rate achieves the ventilation required for the actual occupancyn the space. In practice, the design ventilation rate is calculatedased on the assumption of a CO2 equilibrium condition (steadytate) and occupant density that might be very different from actualne, which could distance indoor CO2 concentration from the CO2et point (e.g. 1000 ppm recommended by ASHRAE 62-2007). Inact, both approaches are far more optimal in terms of energyaving. An ideal control approach should keep indoor CO2 concen-ration as close as possible to the CO2 set point during occupiederiods.

In this paper, we develop a new control strategy of CO2-basedCV for sports training arenas to address the challenge of controllgorithms. Further, CO2-based DCV has been widely applied tochools, conference rooms, restaurants, theatres and office spaces18], but sports training arena was hardly mentioned in spitef the fact that it is widely practiced. In Finland, for example,here are over 2000 indoor sports facilities and a big part of themre sports training arenas, which present a potential sector ofnergy saving. In addition, energy control strategies for sportsraining arenas are certainly applicable to a big range of build-ngs/spaces such as classrooms, theatres, conference rooms ando on. These buildings/spaces have a common feature that theirpening hours are dominated by schedules, that is, occupied andnoccupied hours are well scheduled and known in advance. Thiseature offers an opportunity for CO2-based DCV to utilize sched-les fashionably. The new strategy was exactly developed for thisurpose.

In a sports training arena, opening hours are composed of train-ng sessions (occupied period) and breaks (unoccupied period). Theime table for training sessions is known beforehand, but the num-er of occupants for each training session is unknown. The newtrategy gives a base ventilation rate for breaks and unoccupiedours, and at each training session, estimates the required ventila-ion rate by solving the CO2 mass balance equation so that indoorO2 concentration is steadily going up and finally approaches theO2 set point (e.g. 1000 ppm) at the end of the session. As such, theontrolled CO2 concentration by the new strategy is close to theO2 set point at each training session, therefore the ventilation isptimized. The objectives of this study are:

to develop a concise strategy to implement CO2-based DCV forsports training arenas and similar kind of buildings. The calcu-lated ventilation rate from the new strategy also should meetthe recommendation of the minimum requirement of outdoor

gs 43 (2011) 2499–2508

air supply rate per person from some industrial standards, suchas ASHRAE 62-2007 [11];

• to extend the developed strategy to more common buildings.

The developed approach is simple, economical and can be usedas an alternative for conventional CO2 DCV control algorithms.

2. Methodology

In this paper, we assume indoor air is well mixed as it is a com-mon assumption for buildings. The methodology discussed in thispaper focuses on single zone systems, but can be adapted to mul-tiple zone systems easily. In addition, for the sake of simplicity,infiltration or exflitration is not considered in this study on theassumption that the supple air is balanced with the return (exhaust)air. In CO2-based demand-controlled ventilation, equilibrium anal-ysis is frequently used to calculate ventilation rate from indoor CO2level. This technique is based on a mass balance of CO2 in the build-ing/space. For a mechanically ventilated space, the mass balance ofCO2 concentration can be expressed as:

VdC

dt= Q (Co − C(t)) + G(t) (1)

where V is the space volume, C(t) the indoor CO2 concentration attime t, Q the volumetric airflow rate (fresh air) into (and out of), Co

the outdoor CO2 concentration, and G(t) is the CO2 generation rateat time t. If we assume Q, Co and G(t) are constant, Eq. (1) can besolved as follows:

C(t) = Co + G(t)Q

+(

C(0) − Co − G(t)Q

)e−It (2)

where C(0) is the indoor CO2 concentration at time 0, I = Q/V, airchange rate. Eq. (2) is used as an essential tool to calculate the ven-tilation rate dynamically in this study when C(t), Co, G(t) and t areknown. If the CO2 generate rate is constant for a sufficient time,the last term on the right side of Eq. (2) converges to zero, and theequation can be rewritten as:

Ceq = Co + G

Q(3)

where Ceq is called as the equilibrium CO2 concentration. Eq. (3) isreferred as equilibrium analysis as we mentioned previously. In CO2-based control approach, Ceq is taken from a preset CO2 level, alsocalled CO2 set point, which the indoor CO2 concentration shouldbe kept below, and G is the design generation rate, namely gener-ation rate per person multiplied by possibly maximum number ofoccupants. The resulted Q from Eq. (3) is the required design venti-lation rate for the space. Moreover, the estimation of the number ofoccupants can be done by solving Eq. (1) directly. A more accurateand dynamic estimation (number of occupants), called dynamicdetection, was developed by Wang and Jin [10].

In the later version of the ventilation standards (ASHRAE62-2007 [11]), the minimum requirement for the outdoor air ven-tilation rate is suggested as:

DVR = RpP + BASE R (4)

where DVR is the demanded (minimum) ventilation flow rate, P isthe number of occupants, Rp is the fresh air requirement per person,and BASE R is the base ventilation for unoccupied hours. BASE R isapplied to dilute non-occupant-generated pollutants.

2.1. Sports training arenas

A typical sports training arena includes a primary zone, traininghall, and a recreation zone. Sports activities happen in the train-ing hall, which is also our focus in this study. In a sports training


T. Lu et al. / Energy and Buildings 43 (2011) 2499–2508 2501

Fig. 1. One day’s schedule from an indoor ice rink.

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be not less than 50% [11]. Practically the governing equation (i.e.Eq. (1)) can be improved by taking ventilation effectiveness intoaccount, namely considering the CO2 concentration at the breath-

Fig. 2. The illustration of training session, break, sample time

rena, open hours are made up of some training (sport) sessions andreaks. Fig. 1 shows one day’s time table from an indoor ice rink.he period between two training sessions is called break (Fig. 1),hich lasts from 10 to 15 min in training dense periods of one day

in the evening) and much longer time in other periods. When areak comes, training teams or individuals have to leave and ser-ice people (normally one person) come to clean the sport groundnd provide some maintaining services, such as resurfacing of thece in indoor ice rinks. In general, we can regard break as unoc-upied period. In addition, the time table of training sessions cane obtained beforehand, but the number of occupants in a session

s unable to be known. These features present challenges but alsootential for energy saving if we can optimally utilize them.

.2. Details of the new strategy for CO2-based demand controlentilation

Firstly let us define and explain some basic terminologieshrough the illustrations of the figures (Figs. 2 and 3).

In Fig. 2, sample time is defined as the time for reading indoorO2 concentration from the sensor and fetching the training session

nformation from the schedule database. Sample interval is defineds the time difference between two sample times. Training remain-ng time (Ttsr, see Fig. 2) is the time period between the sample timend the end time of a training session. Training remaining time sup-lement (Ttss) is a supplement time period which can be any valueut often we set it as smaller than 10 min. When calculating theentilation rate, we intend to use Ttss (training remaining time sup-lement) to extend Ttsr (training remaining time) in case the indoorO2 concentration exceeds the CO2 set point at the end of a trainingession. CO2 set point supplement (Cssco2, Fig. 3) acts as in a similarashion as Ttss, except that it is used to lower the CO2 set point so aso prevent the indoor CO2 concentration from exceeding the CO2 setoint at a training session. The new method calculates ventilationate in two ways:

. If sample time is in a break, set the base ventilation as the ven-tilation rate.

. If sample time is in a training session, the method goes throughthe following steps:

Step 1: Estimate the number of occupants by directly solving Eq.(1).Step 2: Calculate the minimum requirement for the outdoor airventilation rate based on Eq. (4). This step ensures the minimum

ng remaining time, and training remaining time supplement.

ventilation requirement of outside air by industry standards, suchas ASHRAE 62-2007.Step 3: Calculate the ventilation rate via Eq. (2) by setting:C(0) = measured indoor CO2 concentration at the sample time,G = estimated from Eq. (1), t = Ttsr + Ttss, and C(t) = Csco2 (CO2 setpoint) − Cssco2.Step 4: Select the maximum value from calculated ventilation ratesin Steps 2 and 3 as the building/space ventilation rate.

Note that two supplements (Ttss and Cssco2) provide double secu-rities to assure the indoor CO2 concentration below the CO2 setpoint and the maximum Cssco2 can be calculated based on the sam-ple interval, the base ventilation rate and the maximum designnumber of occupants. In practice, however, a 0–10 ppm for Cssco2is acceptable. Hence Ttss can be set between 0 and 10 min. In anycase, Ttss and Cssco2 need to be adjusted in order to get more opti-mal result. The developed approach can be well illustrated by a flowchart in Fig. 4.

The ‘well-mixed’ assumption in this paper is just for the pur-pose of the simple and convenient illustration of the new strategy,which is acceptable only for small spaces in practice. The term ‘ven-tilation effectiveness’ is used to measure how well the outdoor airmixes with the breathing zone for removal of CO2 or other pollu-tants, which is defined by the fraction of the outdoor air deliveredto the space that reaches the breathing zone [11,19]. By this defini-tion, the ‘well-mixed’ condition means ventilation effectiveness isapproaching 100% (Note: ventilation effectiveness may be greaterthan 1 [11]). ASHRAE suggests the ventilation effectiveness should

Fig. 3. The illustration of CO2 set point and CO2 set point supplement.


2502 T. Lu et al. / Energy and Buildings 43 (2011) 2499–2508

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Fig. 4. The flow chart of the new strategy (Note: please check corre

ng zone is different from that in the return air ([19,20] for theetail). This improved simple model reflects the average situationf CO2 concentrations at the breathing zone and has been provedo be feasible for large spaces, where the indoor air may be not

ing equations from the paper based on their numbers in the chart).

well-mixed and CO2 sources are not distributed uniformly, by manyresearches and experimental studies [19,20]. For more complicatedcases, Eq. (1) can be further enhanced by two zone or multizonemodel [20].



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.3. Implementation of the new strategy

The location of indoor CO2 sensor can be mounted either in thereathing zone or in the return air duct (see Fig. 5). The former loca-ion is more accurate, but it lacks reliability as it may be too close tohe occupants. As the CO2 concentration in the return air duct is notame as that at the breathing zone due to shot circuit of the outdoorir, the CO2 relationship between two locations has to be addressed.xperimental studies suggest that it is possible to experimentallybtain a linear correlation between the CO2 concentration in theeturn air duct and that at the breathing zone [20,21]. Therefore, anverall correlation between the CO2 concentration in the return airuct and averaged CO2 concentration from several representative

ocations at the breathing zone should be made beforehand. Thisay, the new strategy can possibly keep the average of CO2 con-

entrations (or CO2 concentrations) at the breathing zone below orear the set point at a training session using the return duct CO2ensor. Alternatively, besides the overall correlation, each individ-al CO2 correlation between the return air duct and representative

ocation at the breathing zone is also developed. The new strategyan use individual correlations to obtain the highest correspondingO2 concentration at the breathing zone based on the CO2 sensoreading in the return air duct, which CO2 concentrations at repre-entative locations will be possibly controlled below or near theet point at the training session. However, this alternative needso be verified in practice and is used with caution because differ-nt distribution patterns of CO2 sources at the breathing zone mayenerate the same CO2 concentration in the return air duct in someuildings. In real sports halls, a DCV control system only needs toontrol the CO2 concentration in the return air duct below someet point. 800 ppm is quite common set point in the return air ductn sports halls (e.g. in Finland). The new strategy can also work likehat and will keep the CO2 concentration in the return air duct much
loser to the set point (e.g. 800 ppm).
The implementation of the new strategy can be done in manyays. One possibility is to use a computer to control the damper

f the ventilation system via connections to indoor/outdoor CO2

plementation of the new strategy.

sensors and schedule database. Fig. 5 illustrates this idea. In Fig. 5,the new strategy is implemented as the computer program whichcan adjust the position of the fresh air damper based on theobtained information from the indoor CO2 sensor and scheduledatabase. For the sake of accuracy, an outdoor CO2 sensor can beconnected to the computer.

3. Experimental study

This experimental study was conducted over a sports trainingcenter in Finland. Due to time and condition limits, experimen-tal data were obtained through software simulations using bothsimulated and experimental CO2 generation rates in Matlab. Thesports training center has two zones: training zone and recreationzone. The simulations were made for the training zone. Because thetraining zone is supplied with 100% outdoor air, we can regard theventilation system as a single zone system. The information aboutthe sports training center is as followed:Opening hours: 7 a.m. to 23:00 p.m.The volume of training zone: 17,220 m3

Design occupancy of training zone (maximum occupancy): 80Guideline value for minimum requirement of outdoor air: 8 dm3/s per person

The guideline value for the minimum requirement of outdoorair was taken from The National Building Code of Finland [22].Since the explicit value for the minimum requirements (per per-son) of outdoor air in sports arenas is not specified in the buildingcode, we used the standard guideline value, 8 dm3/s per person,from the building code [22] for the training area. Normally a train-ing session lasts 1 h with a 10–15 min break, though it may lastover 2 h sometimes. The base ventilation rate and the maximumpossible ventilation for the ventilation system are assumed to be0.26 m3/s and 3.48 m3/s respectively based on the actual ventila-tion system. The sample interval was 5 min. Two control strategies,
proportional control algorithm and the new strategy discussed inthe previous section, were simulated and compared. As mentionedin Section 1, although exponential control has better control per-formance, it does not bring additional benefit in terms of energy


2504 T. Lu et al. / Energy and Buildings 43 (2011) 2499–2508

Fig. 6. One day’s experimental CO2 generation rates.

Fig. 7. Comparison of simulated indoor CO2 concentrations between the new strategy and the proportional control in the sports training arena (simulated CO2 generationrates, 14 days).

Fig. 8. Occupant profile vs. ventilation rate calculated by the new strategy i
n the sports training arena (simulated CO2 generation rates, one day).


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aving and its implementation is more difficult. Therefore, we choseroportional control strategy for a simple illustration of the conven-ional strategy. Furthermore, the outdoor CO2 concentration waset as 450 ppm (constant) and CO2 set point as 800 ppm, namely,he indoor CO2 concentration was kept below 800 ppm. We usedhe formula based on Standard 62.1-2007 User’s Manual [18] formplementing the proportional control algorithm. The formula is:

DCV = (Cs-actual − Cs-min) × (Vot-design − Vot-min)(Cs-design − Cs-min)

+ Vot-min (5)

here VDCV is the ventilation rate provided by a proportionalontrol algorithm, Vot-min the base ventilation rate for non-ccupant-related pollutants, Vot-design the design ventilation ratemaximum occupancy multiplied by design per person ventilationate), Cs-min the target indoor CO2 concentration at the base venti-ation rate, Cs-max the target indoor CO2 concentration at the designentilation rate (indoor CO2 set point), and Cs-actual is the actualndoor CO2 concentration.

Therefore, Vot-min, Cs-min and Cs-max were set as 0.26 m3/s,50 ppm and 800 ppm separately. The average CO2 generation rateer person at a training session was assumed to be 30 L/h duringhe course of excise work.

.1. Using simulated CO2 generation rates for training sessions

This section refers to the experimental simulation with simu-ated CO2 generation rates for training sessions, and the followingection (Section 3.2) discusses the experimental simulation withxperimental CO2 generation rates. The design ventilation rateer person was obtained as 0.024 m3/s from Eq. (3) by settingeq = 800, Co = 450 and G = 30 L/h (see Section 3). Based on thesealues, the design ventilation rate, Vot-design, was estimated as.92 m3/s = 0.024 × 80.

In addition, we have also updated the proportional controlpproach through

. Setting ventilation rate = base ventilation rate for the breaks andunoccupied periods.

. Calculating the ventilation rate from Eq. (5) for the training ses-sions.

Training schedules for about 2 weeks were taken from the realchedules. CO2 generation rates at each training session were gen-rated via normal distribution (the mean = 30 L/h × the number ofccupants) with ascending order, which presents the worst case forhe newly developed strategy. Note that the maximum number ofccupants would approximately be 63 occupants from the sched-les if the average CO2 generation rate per person is set as 30 L/h.tss (training remaining time supplement) and Cssco2 (CO2 set pointupplement) were set as 5 min and 5 ppm respectively. Two differ-nt design ventilation rates, 1.92 m3/s (actual design ventilation)nd 1.52 m3/s, for the proportional control approach were exam-ned. The latter design ventilation corresponds to 63 occupants withhe assumption of 30 L/h CO2 generation rate per person, whichepresents a very ideal setting of the design ventilation rate as its near the design ventilation rate calculated based on the actual

aximum number of occupants (i.e. 63 occupants). This kind ofdeal setting hardly occurs in practice because the design occu-ancy often has a considerable distance from the actual one inrder to assure the indoor CO2 concentration below the CO2 setoint for the whole control period. The reason why we examined
his ideal setting, 1.52 m3/s, is to show how efficient the new strat-gy is compared to the conventional control approach, for example,roportional control strategy. The following is a brief summary ofhe above discussed information and results:
gs 43 (2011) 2499–2508 2505

Outdoor CO2 concentration: 450 ppmCO2 set point: 800 ppmBase ventilation rate: 0.26 m3/sDesign ventilation rate (proportional controlapproach):

1.92 m3/s (80 occupants)1.52 m3/s (63 occupants)

Eq. (2) was used to generate sample data using the mathematicalscript language in Matlab environment. The new strategy involvessolving Eq. (2) to obtain the required ventilation rate, Q (Fig. 4).However, it is very difficult to solve Eq. (2) analytically. In orderto solve this problem, in the process of simulation we divided thedifference between the maximum and base ventilation rates inton steps. At ith step, the ventilation rate was calculated as: baseventilation rate + i × (maximum ventilation rate − base ventilationrate)/(n − 1). At each sample time, each step of ventilation rate wasselected (from the smallest to the biggest) to feed Eq. (2) until thesmallest step of ventilation rate was found to keep CO2 concentra-tion very near the set point at the end of the training session. Thisnumerical method is simple and more close to the reality. In oursimulation, the step size, n, was set as ten. Step size can also be setas a big number in order to pursue more accurate results.

3.2. Using experimental CO2 generation rates for training sessions

Since it is very difficult to simulate the exact pattern of CO2generation rates in a training session with the existing software,we adopted experiments to assist the simulation. Indoor CO2 con-centrations were measured in a vocational school gym, which issupplied with 100% outdoor air with a throughout constant venti-lation rate (i.e. approximate 1.3 m3/s). As the outdoor CO2 could beestimated by observation, the experimental CO2 generation rates,therefore, were easily obtained from the measured CO2 concentra-tions with the information of schedules.

Although estimation errors in CO2 generation rates wereinevitable, the pattern and trend of actual CO2 generation rates in atraining session could largely be reserved, which is more significantfor this study. Unlike conventional training arenas, the occupiedperiods in the vocational school gym were not very well scheduledwhich, for example, involved classes, team trainings, and randomexercises. In order to make occupied periods in the gym more liketraining sessions, the short occupied periods which included onlyrandom excises were eliminated and the occupied periods relatedto classes and training were kept. As a result, the training sessionsin the gym were long and not densely distributed. Fig. 6 shows oneday’s processed CO2 generation rates in the gym. In each “train-ing session”, the CO2 generation rate oscillated. Experimental CO2generation rates for 2 weeks’ period were then applied over thesame sports training center as previously described. The designventilation rate was set as 3.17 m3/s based on the maximum CO2generate rate (i.e. 4000 L/h). Other conditions were kept the sameas described in Sections 3 and 3.1.

4. Simulation results and discussion

In order to compare the energy consumption associated with thedifferent control strategies, in this paper energy saving accounts foronly the part of energy which is used to condition the ventilationair to the indoor conditions.

Figs. 7 and 8 show results for about 2 weeks’ simulated CO2concentrations using simulated CO2 generation rates and one day’sventilation rates from the newly developed strategy against theoccupant profile respectively and Table 1 comparison results of the
average ventilation rates from the proportional control approachand the new strategy. The simulated CO2 concentrations usingexperimental CO2 generation rates are given in Fig. 9, and Table 2lists comparison results of the average ventilation rates from the


2506 T. Lu et al. / Energy and Buildin

Table 1Comparison of average ventilation rates between the new strategy and proportionalcontrol (simulated CO2 generation rates).

Control approach Averageventilation rate(m3/s)

Average CO2

concentrationduring openinghours(7 a.m.–23 p.m.)

Proportional control (1.92 m3/s)a 0.72 628 ppmProportional control (1.52 m3/s)a 0.65 648 ppmNew strategy 0.43 749 ppm

a The number in parentheses is the design ventilation rate.

Table 2Comparison of average ventilation rates between the new strategy and proportionalcontrol (experimental CO2 generation rates).

Control approach Averageventilation rate(m3/s)

Average CO2

concentrationduring openinghours(7 a.m.–23 p.m.)

Proportional control (3.17 m3/s)a 0.64 588 ppm

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New strategy 0.39 664 ppm

a The number in parentheses is the design ventilation rate.

roportional control approach and the new strategy for experimen-al CO2 generation rates.

As we can see in Fig. 7 and Table 1, even though the propor-ional control algorithm was assigned with a very ideal designentilation rate (i.e.1.52 m3/s) and was further optimized by apply-ng schedules, the indoor CO2 concentration is still below 700 ppm

ost of the times, which is far away from the CO2 set point –00 ppm. The main reason is that the ventilation rate calculatedy the proportional control is based on the assumption of equilib-ium condition (steady state), which seldom occurs in practice. As

consequence, the space is often over ventilated although the min-mum ventilation rate of outdoor air can be satisfied based on thendustry standards. In contrast, the newly developed strategy takeshe dynamic effects into account by solving the CO2 mass balancequation (Eq. (2)) for calculating the instantaneous ventilation rate.s a result, the controlled CO2 concentration is much closer to theO2 set point (see Fig. 7). This phenomenon is more strongly mani-
ested in training dense periods (after 5 p.m.) where the indoor CO2oncentration is mostly controlled between 770 ppm and 800 ppmt each training session (Fig. 7). Table 1 clearly shows that theew strategy can respectively save 40% (≈(0.72 − 0.43)/0.72) (vs.
ig. 9. Comparison of simulated CO2 concentrations between the new strategy and the pr4 days).

gs 43 (2011) 2499–2508

1.92 m3/s) and 34% (≈(0.65 − 0.43)/0.65) (vs. 1.52 m3/s) ventilationcompared to the proportional control approach, meaning that +34%of energy related to ventilation air can be saved.

Fig. 8 shows that the ventilation rate changes with the changesof occupants although some sessions experience fluctuations ofventilation rate. Fluctuations of ventilation rate clearly stem fromthe changes of activity level and may occur often in practice.Nevertheless, the new strategy provides a certain ventilation ratedynamically based on the temporal pattern of occupancy profiles.Because CO2 generation rate changes considerably with the activitylevel, it is very difficult to determine the exact number of occupantsalthough some dynamic detection [10] was proved to improveon the accuracy. But, underestimation or overestimation on thenumber of occupants is not very essential in the new strategy aseventually the ventilation rate will be well distributed over a train-ing period so that the indoor CO2 concentration is kept below theCO2 set point at the training session. From the energy conserva-tion point of view, the new strategy is more flexible and efficient.Moreover, one may find that the new strategy experiences delays inventilation control (Fig. 8), and the delay happens either at the startof a session or inside a session. This is because in the non-steady-state (transient) condition, the change of indoor CO2 concentrationwill generally lag behind changes in the actual number of occu-pants. When the number of occupants varies, the space is eitherunder-ventilated or over-ventilated at a moment since the indoorCO2 concentration does not change instantaneously. In this study,the CO2 generation rate in each session rises in an ascending fash-ion, which could cause the ventilate rate underestimated and as aconsequence the CO2 concentration could exceed the CO2 set pointnear the end of the session. But the new strategy doesn’t suffer fromsuch problem. It works well for this case because two supplements(Ttss, Cssco2, Section 2.2) are added.

Unlike the office work, sports activities are very dynamic. In lit-eratures, there lacks a deep study on the pattern and trend of CO2generation rates in a training session. Based on our preliminaryanalysis for a sports training center, the calculated CO2 generationrate seems to follow an up-peak-down pattern for each training ses-sion although the CO2 generation rate is still very dynamic (Fig. 6).In general, at the end of a session the CO2 generation rate goesdown. If this pattern is true for other training centers, the newstrategy will certainly keep the indoor CO2 concentration below the
CO2 set point even without using the two supplements (Ttss, Cssco2,Section 2.2). In any case, the indoor CO2 concentration can be con-trolled near and below the CO2 set point as long as the appreciatesupplements (Ttss, Cssco2, Section 2.2) are set.
oportional control in the sports training arena (experimental CO2 generation rates,



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ig. 10. Comparison of simulated CO2 concentrations between the new strategy andates, 14 days).

Fig. 9 shows that, with the base ventilation rate (i.e. 0.26 m3/s),he indoor CO2 concentration is mostly below the CO2 set point (i.e.00 ppm), but in one particular “training session”, the indoor CO2oncentration exceeds 1200 ppm, which could be extreme obser-ations or outliers. It is not difficult to see that the proportionalontrol is insufficient for such outliers because the design ventila-ion rate would have been set as a much higher value which makest inefficient in the use of energy. By contrast, in this complex case,he new strategy can still save about 39% (≈(0.64 − 0.39)/0.64) ofnergy related to ventilation air (Table 2). Hence, the new strategys more flexible. The new strategy was validated by experimentalO2 generation rates and proved to be feasible for practical use.

.1. Possibilities to extend the new method to common buildings

It is possible to extend the new strategy to common buildingshich are occupied for most times such as restaurants, leisurelaces, libraries or other public service places. In these buildings,he occupied periods are long but not scheduled.

The introduction of the unscheduled long occupied periods inhe buildings substantially increases the complexity of the controltrategy from a modelling point of view but causes, in fact, only ainor change in the newly developed strategy.We just need to take the whole opening hours as a training ses-

ion and the rest remains the same in the new strategy. As anxample, let us take the training center described in Section 3.e assume that the center is fully occupied with sparse breaks

10–15 min).The simulation is also made for 2 weeks and the maximum num-

er of occupants is near 80 if the CO2 generation rate per person is0 L/h. In addition, we set 1.92 m3/s as the design ventilation rateor the proportional control approach, which is a very ideal settings the design occupancy is almost the same as the actual maximumumber of occupants. The remaining conditions are kept the sames described in Section 3. Simulation results are showed in Fig. 10.he average ventilation rates for 2 weeks with the proportional con-rol approach and new strategy are 0.8 m3/s and 0.59 m3/s, meaninghat the new strategy can save +26% (≈(0.8 − 0.59)/0.8) of energyelated to ventilation air. The new strategy is more efficient thanhe proportional control approach for fully occupied buildings.

Although the new strategy was designed for single zone sys-ems, it can be applied to multiple zone systems also. Here we use

simple multiple zone system as an example to illustrate the newtrategy as well as the comparison with the proportional control

oportional control in fully occupied sports training arena (simulated CO2 generation

algorithm. Supposed that a single air handler system serves 100%outdoor air for multiple zones, where the only control of outsideair is at the air handler. One approach is that the proportional con-trol algorithm takes the zone with the highest CO2 sensor readingas the crucial zone to determine total ventilation rate (outdoor air)so that the crucial zone is satisfied (and, thus, over-ventilate allother zones) [18]. Similarly, the new strategy takes all zones as awhole and always selects the same crucial zone as the proportionalcontrol algorithm does. However, schedules from all zones needto be combined to form a single schedule so that breaks are thoseperiods during which all zones are unoccupied. With the combinedschedule and the CO2 sensor reading from the crucial zone, the newstrategy goes through the same procedures as previously described(Fig. 4) and will save much energy compared to the proportionalcontrol algorithm in any case (see Figs. 7, 9 and 10). The new strat-egy can also be applied to other multiple zone systems in a similarway as conventional CO2-based DCV strategies do.

5. Conclusions

A novel and simple CO2 DCV strategy was developed for sportstraining arenas and similar kind of buildings. The results were ver-ified by simulations using both simulated and experimental CO2generation rates and show that the estimated indoor CO2 concen-trations are much closer to the CO2 set point than those with theproportional control approach, and +34% of energy related to ven-tilation air can be saved. Moreover, the new approach is applicablefor fully occupied buildings with +26% of energy saving related toventilation air. The new strategy improves upon conventional con-trol approaches, proportional and exponential controls, with thefollowing features:

1. Dynamically determine ventilation rate. In the new strategy, theventilation rate is calculated in a transient fashion by solving theCO2 mass balance equation for each training session.

2. Simple and can be easily implemented. The new strategy doesnot require the calculation of design ventilation rate. As weknown, it is very difficult to determine an appreciate design
ventilation rate.
3. Can be extended easily. The new strategy is flexible and appli-cable for not only buildings where opening hours are scheduledbut also other kinds of buildings.


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Obviously the new strategy works more efficiently for shorterraining sessions than longer ones. For buildings that are occupiedor almost all opening hours, the method may not work very effi-iently and there is still much space to improve it. One possibleolution is to divide open hours into some pseudo-training andseudo-break sessions. During pseudo-training sessions, the ven-ilation rate is evaluated in the same way as before, but duringseudo-breaks, set the design ventilation rate as the ventilationate. Because pseudo-training sessions can be set short and theesign ventilation rate could bring the CO2 concentration downt pseudo-breaks, the indoor CO2 concentration would be muchloser to the CO2 set point. In addition, the new strategy is still ints primary stage, more research works need to be done to analyzend identify the limitations of the new strategy with respect to ven-ilation distribution pattern, the improvement for the new strategyhould be also studied accordingly. It is also important to imple-ent our work in practice and add non-occupant-related pollutantonitoring into our work.

cknowledgment

We are grateful to the Academy of Finland for financial support.

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