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A transient ventilation demand model for air-conditioned offices
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APPLIED ENERGY Applied Energy 85 (2008) 545–554 www.elsevier.com/locate/apenergy
A transient ventilation demand model for air-conditioned offices L.T. Wong *, K.W. Mui
1
Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China Received 23 January 2007; received in revised form 24 July 2007; accepted 31 July 2007 Available online 5 November 2007
Abstract Ventilation to supply fresh air in an air-conditioned office consumes a considerable portion of energy in an air-conditioning system and affects the indoor-air quality (IAQ). The ventilation demand is primarily related to the occupant load. In this study, the ventilation demands due to occupant load variations were examined against certain IAQ objectives using the mass balance of carbon dioxide (CO2) volume fractions in an air-conditioned office. In particular, this study proposed a transient ventilation demand model for occupant load, with the parameters determined from a year-round occupant load survey in Hong Kong. This model was applied to evaluate the performance of energy saving in different operating schedules of ventilation systems for typical office buildings in Hong Kong. The results showed that the energy consumption of a ventilation system would be correlated with the transient occupant load and its variations in the air-conditioned office. The ventilation system, with schedules taking account of the transient occupant loads, would offer a reduction in energy consumption up to 19% as compared with an operating schedule that assumed a steady occupant-load in the office during working hours. In both cases, the same IAQ objective was achieved. 2007 Published by Elsevier Ltd. Keywords: Ventilation; Energy saving; System optimization; Occupant load
1. Introduction Hong Kong is located in a subtropical climate region and is one of the most densely populated developed cities in the world. In 2001, its population was 6.71 million and its usable land was 1098.5 km2 [1]. Due to limited land supply and high population, most of the buildings in Hong Kong are high-rise in order to cope with the rapid development of the society. Based on the record of Hong Kong energy statistics, electricity consumption had a multiple increase during the past 33 years [1]. Almost all office buildings in Hong Kong are air-conditioned. As air-conditioning systems consume about half of the total electricity load in office buildings, there is a great concern about the amount of energy consumption by the air-conditioning systems. Energysaving measures concerning the air-conditioning systems have been proposed to enhance the operating efficiency of air-conditioning components [2]. *
1
Corresponding author. Tel.: +852 2766 7783; fax: +852 2765 7198. E-mail addresses: [email protected] (L.T. Wong), [email protected] (K.W. Mui). Tel.: +852 2766 5835; fax: +852 2765 7198.
0306-2619/$ - see front matter 2007 Published by Elsevier Ltd. doi:10.1016/j.apenergy.2007.07.010
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Nomenclature Af i k NF Np Of SD t V_ e Vf V_ g a s / U(t)
floor area (m2) dummy variable for time regression constant number of fans operating number of occupants (ps) average maximum occupant load-factor (m2 ps1) standard deviation time (h) ventilation rate (m3 s1) space volume (m3) CO2 generation rate of occupants (m3 s1) percentage energy-saving (%) time period (h) occupant load-ratio CO2 volume fraction at a time (ppmv)
Superscript fractional parameter of a value between 0 and 1 * Subscripts 0 of initial condition 1, 2 of conditions 1, 2 a, b, f, o of start and end of lunch break and working hours ab, bf, oa, of of period between time a and b, b and f, o and a, or between time o and f i of a time i = a, b, f, o as specified max of maximum OA of outdoor condition t of a time t
The energy crisis in 1973 flagged up the need for finding ways to reduce the energy usage of air-conditioning systems. The overwhelming pressure from government and public, after the crisis, made design engineers overreact to the demand of energy conservation when designing the system and overlooked the issue of indoor health. In order to save energy, they reduced the ventilation rate by outside air from 7.5–10 L s1 per person to 2.5 L s1 per person. Occupational-health problems, related to poor ventilation, were identified as early as the 1960s. Organizations such as US Occupational Safety and Health Association (OSHA), US National Institution of Safety and Health (NIOSH), American Congress of Governmental Industrial of Hygienists (ACGIH), and World Health Organization (WHO) have been conducting studies on the topic. WHO categorized the sick symptoms suffered by occupants in non-industrial workplaces and coined the term ‘Sick Building Syndrome’ (SBS). The SBS was reported in many air-conditioned buildings with poor ventilation [3–7]. With regard to the design of indoor air-conditioning systems, it was pointed out that there was a conflict between the need for energy conservation and the need to create an acceptably clean and healthy indoor-environment in buildings. As a result of the energy crisis, design practices were biased towards designing for energy conservation rather than taking an integrated approach with consideration of comfort and health. A considerable energy consumption in an air-conditioning system for a thermally-comfortable office environment would be due to the ventilation load [8,9]. American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 62-2001 [4], a ventilation standard for acceptable indoor air quality, calls for substantial increases in indoor-air ventilation rates, by as much as 300% above that specified in the
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1981 version [3]. The consequence is a dramatic increase in energy costs for buildings especially in places like Hong Kong where summers are hot and humid and winters are cold and dry. Many engineering systems in buildings and their performances are closely associated with the outdoor conditions and the occupant loads [9,10]. Apart from the indoor design-parameters, occupant load is one of the essential parameters for building designs. Occupant load relates to considerations such as structural loads [11], indoor-air quality (IAQ) and the associated energy consumptions [4], cooling load [9], sewage demand [12], water consumptions [13] and evacuation [14]. Occupant-load surveys in various types of buildings are conducted from time to time in different countries in order to update design practice. Design values or profiles of the occupant load in indoor spaces have been recommended in some design guides and codes of practice [11,15–17]. Courtney and Houghton did a study on the occupant load in typical office buildings in the USA in 1935 [15]. An average maximum occupant load factor Of (m2 person1 or denoted as m2 ps1) ranging from 6 to 15 m2 ps1 (the average was 8 m2 ps1) was recommended for evacuation designs. It is found that many existing building-services engineering system designs were biased in determining the system maximum capacity [16]. Indeed, performance of those systems was strongly related to the time-variant parameter which was not considered properly at the design stage. At the design stage of a typical office building in Hong Kong, the actual occupant-load profile is not disclosed to the building designers. Some engineers would assume a probable maximum occupant load profile for calculating the ventilation loads and corresponding energy-consumptions. For a ventilation system that supplies large quantities of outdoor air to an air-conditioned space, significant amounts of energy could be wasted if the fluctuation of the building occupant load is disregarded [8]. Time-variant occupant-load profiles for office buildings had been investigated with a simulation model proposed [17]. The time-variant occupant-load profiles would be used to examine various ventilation fan operating strategies regarding a ventilation objective and associated energy consumption. As the fresh-air quantity of a ventilation system is related to the occupant load and its variations, optimization of the ventilation system was studied [2]. Optimization of the fresh-air supply quantity from outdoors would be achieved by two control strategies, namely enthalpy-control and demand-control ventilations. However, these control strategies did not consider the corresponding indoor-air pollutant levels during part-load conditions, and therefore dictated a need to develop a practical tool to determine the minimum fresh-air flow rate to prevent an increase of indoor-air pollutants. Apart from a number of common indoor air-pollutants in air-conditioned offices, studies demonstrated that the measurement and analysis of indoor carbon dioxide (CO2) level could be useful for understanding indoor-air quality (IAQ) in air-conditioned and mechanically-ventilated spaces [5–7,18–23]. In an occupied space, the CO2 volume-fraction will increase if the fresh-air supply per person is inadequate. Therefore, CO2 volume fraction is commonly used as a surrogate indicator for assessing the IAQ and ventilation efficiency. In this study, the relationship between the transient-occupant loads and indoor pollutants under different operating schedules of ventilation systems for an air-conditioned office was investigated. As well, the associated energy-saving potential was examined. The ventilation demands due to the transient occupant load were examined against certain IAQ objectives using the ‘mass balance’ model of carbon-dioxide (CO2) volume-fractions in an air-conditioned office. The model parameters for the occupant-load were determined from a yearround occupant-load survey in Hong Kong. This model would be useful in determining the optimal operating schedules of ventilating systems and would improve the efficiency of the design process in a cost-effective manner. It would also provide building managers, control engineers and users a reference in sizing the ventilation system and its equipment, and in evaluating the system’s performance and the energy-saving potential. 2. Ventilation demands For a ‘well-mixed’ mechanically ventilated space of volume Vf (m3), the ventilation rate V_ e (m3 s1) could be determined by limiting the indoor CO2 volume fraction U(t) at any time not exceeding a certain design limit, for example, at 1000 ppmv or 650 ppmv above ambient CO2 volume fraction (assuming the outdoor CO2 volume fraction is 350 ppmv in some existing designs). There will likely be a significant increase in the ambient CO2 volume fraction over the life of an air-conditioning and ventilation system as an average rate
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of increase of 1.4 ppmv per year for the outdoor CO2 was reported [24,25]. A target CO2 volume-fraction of 1050 ppm was proposed for some mechanically ventilated spaces in Hong Kong [26]. The transient CO2 volume fraction U(t) (ppmv) at anytime t (h) for a well-mixed condition could be evaluated by a ‘mass balance’ in the space, at the initial CO2 volume fraction U(t = 0) = U0 (ppmv) and outdoor CO2 volume fraction UOA (ppmv) with the total CO2-generation rate by occupants V_ g (m3 s1), V_ g _ _ UðtÞ ¼ UOA þ ð1Þ 1 eV e t=V f þ U0 eV e t=V f V_ e The CO2-generation rate for a typical office occupant would be taken at 0.00005 m3 s1 and the total CO2 generation rate V_ g (m3 s1) is related to the occupant load. The occupant load during working hours sof (h) in a typical office is transient and the probable number of occupants Np (persons or denoted as ‘ps’) at anytime during these hours t 2 sof V_ g ¼ 0:00005N p
ð2Þ
3. Example office An example of a typical air-conditioned office building in Hong Kong, with a floor area of 1200 m2 and a total building area of 70,000 m2, was referred to in this study [17]. Key information of the office is summarized in Table 1. The year-round occupant load in this office was studied. It was reported that the occupant-load variations could be expressed in the formula below, where Np,max (ps) is the maximum probable number of occupants in the space which can be determined according to the floor area Af (m2) and the occupant loadfactor Of (m2 ps1), and /t is the transient occupant-load ratio at time t expressed as a fraction of the occupant-load factor. N p ðtÞ ¼ N p;max /t ;
N p;max ¼
Af Of
ð3Þ
The working hours sof (h) in the office could be divided into three sessions: morning session soa (h), lunch break sab (h) and afternoon session sbf (h), in which the occupant load variations were significantly different. The notations to and tf are the start time and the end times of working hours, and ta and tb are the start time and the end time of the lunch break 8 sof ¼ tf to > > > < soa ¼ ta to ; tf > tb > ta > to sof ¼ soa þ sab þ sbf ; ð4Þ > sab ¼ tb ta > > : sbf ¼ tf tb Apart from the rapid variations at the start and the end of the working hours and lunch break, the occupant load is relatively steady and its variations expressed by the occupant load ratios /oa, /ab and /bf are small. Fig. 1 shows the example occupant-load ratios /i(t) decreased from /i1 to /i+1 at time t in a period from ti1 to ti+1. For the time approaching the start and the end of the working hours and lunch break, i.e. from ti1 and approaching ti, and the time from ti and approaching ti+1, for i = o, a, b, f, the occupant-load ratios /i would be described below, where /i is a fractional occupant-load ratio between 0 and 1 at time ti, k1 and k2 are regression constants, shown in Table 2 for a unit time block for term increment (t ti) of 5 min, /i ¼ /i1 þ /i ð/iþ1 /i1 Þ;
/i ¼
exp½k i;1 þ k i;2 ðt ti Þ 1 þ exp½k i;1 þ k i;2 ðt ti Þ
ð5Þ
The model basis of Eq. (5) was determined from a year-round occupant-load assessment in a typical high-rise office building in Hong Kong, where the occupant loads of 34 offices and daily and yearly occupant-load ratios at a typical office floor were surveyed [17]. The ventilation system for this office consisted of 4 identical ventilation fans, which could be operated individually and targeted to maintain a ventilation objective so that the average indoor CO2-volume fraction
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Table 1 Characteristics and design conditions of an example air-conditioned office Building characteristics Floor dimension (m) Area per floor (m2) Air-conditioned area per floor (m2) Floor area for each thermal zone (m2) Upper floor perimeter zonea/interior zoneb Typical floor perimeter zonea/interior zoneb Floor-to-floor height (m) Window-to-wall ratio
36 · 36 1296 1200 144.4/393.2 144.4/393.2 3.2 0.4
Design conditions Summer indoor-temperature (C) Relative humidity (%) Supply temperature for cooling (C) Ventilation rate (L s1 ps1) Infiltration (air changes per hour) Ventilation system off Ventilation system on
24 50 14.0 10 0.5 0.1
Occupants Sensible/latent load per person (W) Radiantc/convectived Fraction Equipment load (W m2) Sensible-heat fractione Convective fraction Lighting load (W m2) SWf/LWg radiant fraction Convective fraction Fraction of heat returned to return ducth a b c d e f g h
72.6/59.4 0.5/0.5 12 1 0.8 20 0.24/0.24 0.32 0.2
The zone with cooling load significantly influenced by the heat transfer through building envelope. The zone with cooling load mainly influenced by the heat gains due to occupants, equipment and lighting. Specifies the fraction of the sensible heat gain that is radiative. Specifies the fraction of the sensible heat gain that is convective. Specifies the fraction of heat gain due to equipment that is sensible. The fraction of lighting heat-gain in the short-wavelength (visible) radiation form. The fraction of lighting heat-gain in the long-wavelength (infrared) radiation form. The fraction of total heat that goes directly to the return air duct.
occupant load ratio, φ
τ
,i+1
φ φi(t) φi+1 t
ti
ti+1
time, t
Fig. 1. Occupant-load ratios in a time period around time ti.
would not exceed 650 ppmv above ambient levels at any time during the occupied period. The existing operating schedule (Schedule 1) is shown in Table 3. This operation schedule assumed full occupant-load during office hours. The ventilation fans would be turned on an hour prior to the nominal office hours and shut down about an hour after the nominal office hours, i.e. from 0800 to 1900 in this office. In actuality, the occupant
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Table 2 Occupant load model parameters of Hong Kong offices Model parameters Occupant load, lnðO1 f Þ Occupant load ratios at a time Occupant load ratios in a time Occupant load ratios in a time Occupant load ratios in a time Model constant at time to, ko,1 Model constant at time to, ko,2 Model constant at time ta, ka,1 Model constant at time ta, ka,2 Model constant at time tb, kb,1 Model constant at time tb, kb,2 Model constant at time tf, kf,1 Model constant at time tf, kf,2
Average (standard deviation) 2.680 (0.426) 0.815 (0.022) 0.950 (0.029) 0.470 (0.040) 0.919 (0.020) 0.636 (0.225) 5.909 (1.022) 0.085 (0.320) 8.200 (2.503) 29.02 (7.127) 117.0 (0.0003) 1.682 (0.387) 5.428 (1.039)
t, /t=10:30,14:30 period soa, /oa period sab, /ab period sbf, /bf
Table 3 Operating schedules of the ventilation system in the example office Time
Number of ventilation fans operated Schedule 1 (existing)
Schedule 2
Schedule 3
Schedule 4
Schedule 5 (proposed)
0 4 4 4 4
0 0 4 4 4
0 0 0 4 4
0 0 0 0 4
0 0 1 3 4
(B) Lunch and afternoon sessions 12:46–13:00 4 13:01–13:15 4 13:16–14:00 4 14:00–17:45 4
3 3 3 4
2 2 2 4
4 4 4 4
3 3 2 4
(C) Evening session 17:46–18:00 18:01–18:15 18:16–18:45 18:46–19:00 After 19:00
4 4 4 4 0
3 3 3 3 0
2 2 2 2 0
4 4 4 4 0
3 2 1 0 0
Total fan-hours (% saving)
44 (0%)
39.5 (10%)
36 (18%)
40 (9%)
35.8 (19%)
(A) Morning session Before 8:00 8:01–8:30 8:31–8:45 8:46–9:00 9:01–12:45
load would vary from time to time and would not be full at any time. It was therefore suggested to optimize the energy use by re-scheduling the operation schedule of the ventilation fan(s) with reference to the designed maximum-CO2 level. A number of proposals for fan-operation optimization in the morning session, the lunch session and the evening session were suggested and are summarized in Table 3 for illustration. Compared with the current operating schedule, Schedule 2 delayed the fan’s starting time by half an hour, switched off one of the four fans during lunch hours and after office hours. Schedule 3 delayed the fan’s starting time by 45 min and switched off two fans during lunch hours and after office hours. Schedule 4 started the fans at the same time as the office hour in the morning and stayed the same throughout the day. Schedule 5 was an example of varying fan-operation for maintaining the probable CO2 levels below a certain limit for this office (see Fig. 2).
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Number of occupants Np (ps)
100
80
60
40
20
Simulated load Observed load
0 6
9
12
15
18
21
Time (hours) Fig. 2. Occupant-load profiles.
4. Results and discussion The performances of the proposed ventilation schedules in Table 3 can be evaluated using the time-variant occupant-load profiles generated for the example office. The probable occupant-load profiles were obtained by simulations of the example office for 800 working days using Eqs. (3)–(5). Using the probable-load profiles, Fig. 3 shows the simulated daily CO2 levels using the operating schedules for the ventilation system during the transient periods, i.e. at (a) beginning of the workday, (b) the lunch period and (c) end of the workday. A new day started with a low level of indoor CO2. The CO2 in the air was probably the residue left behind from the previous day. With the effect of infiltration/exfiltration, the indoor CO2 level was approaching the outdoor ambient CO2 level. Most of the occupants arrived at the office at around 08:30 and the CO2 volume fraction rose correspondingly. At the design occupant-load, with full operation of all 4 ventilation fans, the indoor CO2 would rise to a morning steady-state, which was about 650 ppm above ambient levels as shown in Fig. 3(a). An early start of the ventilation system (Schedule 1) would prevent a sudden rise of the indoor CO2 volume fractions to an extreme peak. Conversely, a late start of the ventilation system would result in failure to keep the morning peak CO2 level below the design CO2 level, i.e. 650 ppm above the ambient levels as stated in the ASHRAE standard [4]. The demands of ventilation could be determined by comparing the morning peak of the indoor CO2 to the design CO2 level. The results showed that satisfactory ventilation would be achieved if the ventilation system started 15 min before the office hours in the example office. Near noon period, some office worker’s leave at various times for lunch, and some stay behind. The results in Fig. 3(b) show that the indoor CO2-volume fractions decrease, due to the decreased load. The indoor CO2 level dropped to a minimum at around 14:00. The ventilation rate could be reduced to achieve energy savings during the lunch break. Compared with the existing operation scheme (Schedule 1), Schedule 2 (shut down one ventilation fan at lunch break) was an example of reducing the fresh-air supply quantity without compromising the indoor environment quality, keeping the indoor CO2 volume fraction less than or equal to 650 ppm above ambient levels. However, further reduction in the ventilation fans operation was not recommended as the peak indoor CO2 volume fractions could be excessive. A combination of the two fan operation schemes would be considered for optimization. Some occupants started to leave at the end of the working day in the example office, i.e. at 17:00, while others would stay after the normal office hours. The indoor CO2-levels decayed slowly as shown in Fig. 3(c). The results showed that the fresh-air rate could be reduced for optimization. Two alternative operation schedules (Schedules 2 and 3) were considered, each with 1 or 2 ventilation fans shut down, and the corresponding simulated CO2 levels were shown in the figure. As indicated, the indoor CO2 levels would be unsatisfactory when more than one ventilation fan was shut down, as in the case of Schedule 3.
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CO2 volume fractions (ppmv)
2400 Schedule 4
2000 1600
Schedule 3 1200 Schedule 2
800 400
Schedule 1
0 6
8
10
12
Time (hours) 1300
CO2 volume fractions (ppmv)
Schedule 3 1100
900
700
Schedule 2
500
Schedule 1
300 12
13
14
15
Time (hours) 1300
CO2 volume fractions (ppmv)
Schedule 3 1100 Schedule 2 900 Schedule 1
700
500
300 17
18
19
20
Time (hours) Fig. 3. Predicted CO2 volume fractions in a working day: (a) beginning of workday, (b) lunch break, (c) end of workday.
After considering the simulated CO2-level results of the alternative operations in Schedules 2–4, an operation schedule (Schedule 5) was suggested to achieve the ventilation objective. This operation schedule was designed to keep the minimum number of ventilation fans in operation, while maintaining the indoor CO2
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1300
CO2 volume fractions (ppmv)
Proposed schedule 1100
Proposed schedule
Proposed schedule
900
700 Existing schedule
500
Existing schedule
300 7
10
13 Time (hours)
16
19
Fig. 4. CO2 volume fractions with example operating schedules of ventilation system in the sample office.
volume-fraction at a level not exceeding 650 ppmv above ambient CO2 levels. A comparison of the two different CO2 levels in the example office, with the ventilation system operating according to the existing schedule and the proposed schedule, is shown in Fig. 4. The probable energy-saving a(%) with the proposed schedule compared with the existing schedule would be approximated by the total ventilation-fan operating hours of the proposed schedule and the existing schedule, where, NF1 and NF0 are the number of fans operating in time i of the proposed schedule and the existing schedule, respectively, P N F1;i a ¼ Pi ð6Þ i N F0;i The total fan operating hours for Schedules 1–5 were determined, with the results summarized in Table 3. Compared with some design norms assuming full occupant-load when designing the office ventilation system, energy savings of the ventilation fan operation with consideration of the occupant load variations in the office could be significant. In this study, using the example office with some occupant-load variation patterns accounted, it was shown that by varying the ventilation fan operating schedule through simple on–off operation of some ventilation fans could result in energy savings up to 19% without any compromise of indoor-air quality. 5. Conclusions Simulations, based on realistic input parameters, could be a very useful technique in evaluating the annual energy consumption and system control strategies for various ventilation systems of air-conditioned offices. In this study, the ventilation demands of an HVAC system due to occupant-load variations were examined against certain IAQ objectives using the mass balance of carbon-dioxide (CO2) volume fractions in an office. The occupant-load variations were reported for the surveyed office during lunch hour and at the beginning and the end of the workday. The variations would have a significant relationship with the ventilation demands of the ventilation system. The transient occupant-load model was used as a basis for evaluating the energy performance of different operating schedules of ventilation systems in typical office-buildings of Hong Kong. The results show that the transient occupant-load in the space is an explanatory parameter in determining the indoor CO2-levels and is therefore an influencing factor on the energy consumption of the ventilation system. Compared with an operating schedule assuming the steady occupant-load in the office during working hours, alternative fan-operating schedules would lead to a reduction in energy consumption of the ventilation fans up to 19%, with the IAQ objective achieved. The model would be a useful source of information for the policymaker to evaluate current operating strategies of ventilation systems in offices. This paper also provides a
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template for evaluating relative energy consumptions of various ventilation strategies for air-conditioned spaces with transient occupant-loads against certain IAQ objectives with a design maximum CO2 volume fraction specified. Acknowledgements The work described in this paper was partially supported by a grant form the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No: PolyU5248/06E, Account Code: BQ01G) and by a grant from The Hong Kong Polytechnic University (Project Account Code: GYE80). References [1] Census and Statistics Department. Hong Kong Energy Statistics, Annual Report 1970–2002. The Government of the Hong Kong Special Administrative Region, China, 2004. [2] Mui KW, Chan WT. A pilot study for the performance of a new demand control ventilation system in Hong Kong. ASCE J Archit Eng 2005;11(3):110–5. [3] ANSI/ASHRAE Standard 62-1981. Design for acceptable indoor-air quality. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers; 1981. [4] ANSI/ASHRAE Standard 62-2001. Design for acceptable indoor-air quality. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers; 2001. [5] ASTM. Standard guide for using indoor carbon-dioxide concentrations to evaluate indoor-air quality and ventilation. D6245 – 98, 2003 [Reapproved 2002]. [6] Jokl MV. Evaluation of indoor-air quality using the decibel concept based on carbon dioxide and TVOC. Build Environ 2000;35(8):677–97. [7] Persily AK. Evaluating building IAQ and ventilation with indoor carbon-dioxide. ASHRAE Trans 1997;103(2):193–203. [8] Mui KW, Wong LT, Law LY. An energy benchmarking model for ventilation systems of air-conditioned offices in sub-tropical climates. Appl Energy 2007;48(1):89–98. [9] Mui KW, Wong LT. Cooling-load calculations in sub-tropical climate. Build Environ 2007;42(7):2498–504. [10] Kaye NB, Hunt GR. Heat source modelling and natural ventilation efficiency. Build Environ 2007;42(4):1624–31. [11] Bartlett FM, Hong HP, Zhou W. Load-factor calibration for the proposed 2005 edition of the National Building Code of Canada: Statistics of loads and load effects. Can J Civil Eng 2003;30(2):429–39. [12] Wong LT, Mui KW. Discharge demand analysis on a drainage stack for residential buildings. Facilities 2006;24(3–4):132–40. [13] Wong LT, Mui KW. Modelling water consumption and flow rates for flushing water systems in high-rise residential buildings in Hong Kong. Build Environ 2007;42(5):2024–34. [14] Wong LT, Cheung TF. Evaluating probable risk of evacuees in institution buildings. Saf Sci 2006;44(2):169–81. [15] Milke JA, Caro TC. A survey of occupant load factors in contemporary office buildings. J Fire Prot Eng 1997;8(4):169–82. [16] Keith DM, Krarti M. Simplified prediction tool for peak occupancy-rate in office buildings. J Illum Eng Soc 1999;28(1):43–56. [17] Wong LT, Mui KW. Modelling transient occupant loads for offices. Archit Sci Rev 2006;49(1):53–8. [18] Hoskins JA. Health effects due to indoor-air pollution. Indoor Built Environ 2003;12(6):427–33. [19] Mo¨hle G, Crump D, Brown V, Hunter C, Squire R, Mann H, et al.. Development and application of a protocol for the assessment of indoor-air quality. Indoor Built Environ 2003;12(3):139–49. [20] Penman JM. An experimental determination of ventilation rate in occupied rooms using atmospheric carbon-dioxide concentrations. Build Environ 1980;15(1):45–7. [21] Roelofsen CPG. The impact of air quality in offices on employee performance – carbon dioxide as a performance indicator for the perceived air quality. Facilities 2003;21(9/10):43–8. [22] Simon M, Vincenc B. The influence of indoor environment in office buildings on their occupants. Build Environ 2004;39(3):289–96. [23] Wong LT, Mui KW, Hui PS. A statistical model for characterizing common air pollutants in air-conditioned offices. Atmos Environ 2006;40(23):4246–57. [24] Frank T. Climate change impacts on building heating-and-cooling energy demand in Switzerland. Energy Build 2005;37(11):1175–85. [25] Keeling CD, Whorf TP. Atmospheric CO2 records from sites in the SIO air sampling network. In: Trends: a compendium of data on global change. Carbon Dioxide Information Analysis Centre (CDIAC), Oak Ridge National Laboratory, US Department of Energy, 2005. [26] Mui KW, Wong LT. Evaluation of a neutral criterion of indoor-air quality for air-conditioned offices in subtropical climates. Build Services Eng Res Technol 2007;28(1):23–33.
APPLIED ENERGY Applied Energy 85 (2008) 545–554 www.elsevier.com/locate/apenergy
A transient ventilation demand model for air-conditioned offices L.T. Wong *, K.W. Mui
1
Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China Received 23 January 2007; received in revised form 24 July 2007; accepted 31 July 2007 Available online 5 November 2007
Abstract Ventilation to supply fresh air in an air-conditioned office consumes a considerable portion of energy in an air-conditioning system and affects the indoor-air quality (IAQ). The ventilation demand is primarily related to the occupant load. In this study, the ventilation demands due to occupant load variations were examined against certain IAQ objectives using the mass balance of carbon dioxide (CO2) volume fractions in an air-conditioned office. In particular, this study proposed a transient ventilation demand model for occupant load, with the parameters determined from a year-round occupant load survey in Hong Kong. This model was applied to evaluate the performance of energy saving in different operating schedules of ventilation systems for typical office buildings in Hong Kong. The results showed that the energy consumption of a ventilation system would be correlated with the transient occupant load and its variations in the air-conditioned office. The ventilation system, with schedules taking account of the transient occupant loads, would offer a reduction in energy consumption up to 19% as compared with an operating schedule that assumed a steady occupant-load in the office during working hours. In both cases, the same IAQ objective was achieved. 2007 Published by Elsevier Ltd. Keywords: Ventilation; Energy saving; System optimization; Occupant load
1. Introduction Hong Kong is located in a subtropical climate region and is one of the most densely populated developed cities in the world. In 2001, its population was 6.71 million and its usable land was 1098.5 km2 [1]. Due to limited land supply and high population, most of the buildings in Hong Kong are high-rise in order to cope with the rapid development of the society. Based on the record of Hong Kong energy statistics, electricity consumption had a multiple increase during the past 33 years [1]. Almost all office buildings in Hong Kong are air-conditioned. As air-conditioning systems consume about half of the total electricity load in office buildings, there is a great concern about the amount of energy consumption by the air-conditioning systems. Energysaving measures concerning the air-conditioning systems have been proposed to enhance the operating efficiency of air-conditioning components [2]. *
1
Corresponding author. Tel.: +852 2766 7783; fax: +852 2765 7198. E-mail addresses: [email protected] (L.T. Wong), [email protected] (K.W. Mui). Tel.: +852 2766 5835; fax: +852 2765 7198.
0306-2619/$ - see front matter 2007 Published by Elsevier Ltd. doi:10.1016/j.apenergy.2007.07.010
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Nomenclature Af i k NF Np Of SD t V_ e Vf V_ g a s / U(t)
floor area (m2) dummy variable for time regression constant number of fans operating number of occupants (ps) average maximum occupant load-factor (m2 ps1) standard deviation time (h) ventilation rate (m3 s1) space volume (m3) CO2 generation rate of occupants (m3 s1) percentage energy-saving (%) time period (h) occupant load-ratio CO2 volume fraction at a time (ppmv)
Superscript fractional parameter of a value between 0 and 1 * Subscripts 0 of initial condition 1, 2 of conditions 1, 2 a, b, f, o of start and end of lunch break and working hours ab, bf, oa, of of period between time a and b, b and f, o and a, or between time o and f i of a time i = a, b, f, o as specified max of maximum OA of outdoor condition t of a time t
The energy crisis in 1973 flagged up the need for finding ways to reduce the energy usage of air-conditioning systems. The overwhelming pressure from government and public, after the crisis, made design engineers overreact to the demand of energy conservation when designing the system and overlooked the issue of indoor health. In order to save energy, they reduced the ventilation rate by outside air from 7.5–10 L s1 per person to 2.5 L s1 per person. Occupational-health problems, related to poor ventilation, were identified as early as the 1960s. Organizations such as US Occupational Safety and Health Association (OSHA), US National Institution of Safety and Health (NIOSH), American Congress of Governmental Industrial of Hygienists (ACGIH), and World Health Organization (WHO) have been conducting studies on the topic. WHO categorized the sick symptoms suffered by occupants in non-industrial workplaces and coined the term ‘Sick Building Syndrome’ (SBS). The SBS was reported in many air-conditioned buildings with poor ventilation [3–7]. With regard to the design of indoor air-conditioning systems, it was pointed out that there was a conflict between the need for energy conservation and the need to create an acceptably clean and healthy indoor-environment in buildings. As a result of the energy crisis, design practices were biased towards designing for energy conservation rather than taking an integrated approach with consideration of comfort and health. A considerable energy consumption in an air-conditioning system for a thermally-comfortable office environment would be due to the ventilation load [8,9]. American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 62-2001 [4], a ventilation standard for acceptable indoor air quality, calls for substantial increases in indoor-air ventilation rates, by as much as 300% above that specified in the
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1981 version [3]. The consequence is a dramatic increase in energy costs for buildings especially in places like Hong Kong where summers are hot and humid and winters are cold and dry. Many engineering systems in buildings and their performances are closely associated with the outdoor conditions and the occupant loads [9,10]. Apart from the indoor design-parameters, occupant load is one of the essential parameters for building designs. Occupant load relates to considerations such as structural loads [11], indoor-air quality (IAQ) and the associated energy consumptions [4], cooling load [9], sewage demand [12], water consumptions [13] and evacuation [14]. Occupant-load surveys in various types of buildings are conducted from time to time in different countries in order to update design practice. Design values or profiles of the occupant load in indoor spaces have been recommended in some design guides and codes of practice [11,15–17]. Courtney and Houghton did a study on the occupant load in typical office buildings in the USA in 1935 [15]. An average maximum occupant load factor Of (m2 person1 or denoted as m2 ps1) ranging from 6 to 15 m2 ps1 (the average was 8 m2 ps1) was recommended for evacuation designs. It is found that many existing building-services engineering system designs were biased in determining the system maximum capacity [16]. Indeed, performance of those systems was strongly related to the time-variant parameter which was not considered properly at the design stage. At the design stage of a typical office building in Hong Kong, the actual occupant-load profile is not disclosed to the building designers. Some engineers would assume a probable maximum occupant load profile for calculating the ventilation loads and corresponding energy-consumptions. For a ventilation system that supplies large quantities of outdoor air to an air-conditioned space, significant amounts of energy could be wasted if the fluctuation of the building occupant load is disregarded [8]. Time-variant occupant-load profiles for office buildings had been investigated with a simulation model proposed [17]. The time-variant occupant-load profiles would be used to examine various ventilation fan operating strategies regarding a ventilation objective and associated energy consumption. As the fresh-air quantity of a ventilation system is related to the occupant load and its variations, optimization of the ventilation system was studied [2]. Optimization of the fresh-air supply quantity from outdoors would be achieved by two control strategies, namely enthalpy-control and demand-control ventilations. However, these control strategies did not consider the corresponding indoor-air pollutant levels during part-load conditions, and therefore dictated a need to develop a practical tool to determine the minimum fresh-air flow rate to prevent an increase of indoor-air pollutants. Apart from a number of common indoor air-pollutants in air-conditioned offices, studies demonstrated that the measurement and analysis of indoor carbon dioxide (CO2) level could be useful for understanding indoor-air quality (IAQ) in air-conditioned and mechanically-ventilated spaces [5–7,18–23]. In an occupied space, the CO2 volume-fraction will increase if the fresh-air supply per person is inadequate. Therefore, CO2 volume fraction is commonly used as a surrogate indicator for assessing the IAQ and ventilation efficiency. In this study, the relationship between the transient-occupant loads and indoor pollutants under different operating schedules of ventilation systems for an air-conditioned office was investigated. As well, the associated energy-saving potential was examined. The ventilation demands due to the transient occupant load were examined against certain IAQ objectives using the ‘mass balance’ model of carbon-dioxide (CO2) volume-fractions in an air-conditioned office. The model parameters for the occupant-load were determined from a yearround occupant-load survey in Hong Kong. This model would be useful in determining the optimal operating schedules of ventilating systems and would improve the efficiency of the design process in a cost-effective manner. It would also provide building managers, control engineers and users a reference in sizing the ventilation system and its equipment, and in evaluating the system’s performance and the energy-saving potential. 2. Ventilation demands For a ‘well-mixed’ mechanically ventilated space of volume Vf (m3), the ventilation rate V_ e (m3 s1) could be determined by limiting the indoor CO2 volume fraction U(t) at any time not exceeding a certain design limit, for example, at 1000 ppmv or 650 ppmv above ambient CO2 volume fraction (assuming the outdoor CO2 volume fraction is 350 ppmv in some existing designs). There will likely be a significant increase in the ambient CO2 volume fraction over the life of an air-conditioning and ventilation system as an average rate
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of increase of 1.4 ppmv per year for the outdoor CO2 was reported [24,25]. A target CO2 volume-fraction of 1050 ppm was proposed for some mechanically ventilated spaces in Hong Kong [26]. The transient CO2 volume fraction U(t) (ppmv) at anytime t (h) for a well-mixed condition could be evaluated by a ‘mass balance’ in the space, at the initial CO2 volume fraction U(t = 0) = U0 (ppmv) and outdoor CO2 volume fraction UOA (ppmv) with the total CO2-generation rate by occupants V_ g (m3 s1), V_ g _ _ UðtÞ ¼ UOA þ ð1Þ 1 eV e t=V f þ U0 eV e t=V f V_ e The CO2-generation rate for a typical office occupant would be taken at 0.00005 m3 s1 and the total CO2 generation rate V_ g (m3 s1) is related to the occupant load. The occupant load during working hours sof (h) in a typical office is transient and the probable number of occupants Np (persons or denoted as ‘ps’) at anytime during these hours t 2 sof V_ g ¼ 0:00005N p
ð2Þ
3. Example office An example of a typical air-conditioned office building in Hong Kong, with a floor area of 1200 m2 and a total building area of 70,000 m2, was referred to in this study [17]. Key information of the office is summarized in Table 1. The year-round occupant load in this office was studied. It was reported that the occupant-load variations could be expressed in the formula below, where Np,max (ps) is the maximum probable number of occupants in the space which can be determined according to the floor area Af (m2) and the occupant loadfactor Of (m2 ps1), and /t is the transient occupant-load ratio at time t expressed as a fraction of the occupant-load factor. N p ðtÞ ¼ N p;max /t ;
N p;max ¼
Af Of
ð3Þ
The working hours sof (h) in the office could be divided into three sessions: morning session soa (h), lunch break sab (h) and afternoon session sbf (h), in which the occupant load variations were significantly different. The notations to and tf are the start time and the end times of working hours, and ta and tb are the start time and the end time of the lunch break 8 sof ¼ tf to > > > < soa ¼ ta to ; tf > tb > ta > to sof ¼ soa þ sab þ sbf ; ð4Þ > sab ¼ tb ta > > : sbf ¼ tf tb Apart from the rapid variations at the start and the end of the working hours and lunch break, the occupant load is relatively steady and its variations expressed by the occupant load ratios /oa, /ab and /bf are small. Fig. 1 shows the example occupant-load ratios /i(t) decreased from /i1 to /i+1 at time t in a period from ti1 to ti+1. For the time approaching the start and the end of the working hours and lunch break, i.e. from ti1 and approaching ti, and the time from ti and approaching ti+1, for i = o, a, b, f, the occupant-load ratios /i would be described below, where /i is a fractional occupant-load ratio between 0 and 1 at time ti, k1 and k2 are regression constants, shown in Table 2 for a unit time block for term increment (t ti) of 5 min, /i ¼ /i1 þ /i ð/iþ1 /i1 Þ;
/i ¼
exp½k i;1 þ k i;2 ðt ti Þ 1 þ exp½k i;1 þ k i;2 ðt ti Þ
ð5Þ
The model basis of Eq. (5) was determined from a year-round occupant-load assessment in a typical high-rise office building in Hong Kong, where the occupant loads of 34 offices and daily and yearly occupant-load ratios at a typical office floor were surveyed [17]. The ventilation system for this office consisted of 4 identical ventilation fans, which could be operated individually and targeted to maintain a ventilation objective so that the average indoor CO2-volume fraction
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Table 1 Characteristics and design conditions of an example air-conditioned office Building characteristics Floor dimension (m) Area per floor (m2) Air-conditioned area per floor (m2) Floor area for each thermal zone (m2) Upper floor perimeter zonea/interior zoneb Typical floor perimeter zonea/interior zoneb Floor-to-floor height (m) Window-to-wall ratio
36 · 36 1296 1200 144.4/393.2 144.4/393.2 3.2 0.4
Design conditions Summer indoor-temperature (C) Relative humidity (%) Supply temperature for cooling (C) Ventilation rate (L s1 ps1) Infiltration (air changes per hour) Ventilation system off Ventilation system on
24 50 14.0 10 0.5 0.1
Occupants Sensible/latent load per person (W) Radiantc/convectived Fraction Equipment load (W m2) Sensible-heat fractione Convective fraction Lighting load (W m2) SWf/LWg radiant fraction Convective fraction Fraction of heat returned to return ducth a b c d e f g h
72.6/59.4 0.5/0.5 12 1 0.8 20 0.24/0.24 0.32 0.2
The zone with cooling load significantly influenced by the heat transfer through building envelope. The zone with cooling load mainly influenced by the heat gains due to occupants, equipment and lighting. Specifies the fraction of the sensible heat gain that is radiative. Specifies the fraction of the sensible heat gain that is convective. Specifies the fraction of heat gain due to equipment that is sensible. The fraction of lighting heat-gain in the short-wavelength (visible) radiation form. The fraction of lighting heat-gain in the long-wavelength (infrared) radiation form. The fraction of total heat that goes directly to the return air duct.
occupant load ratio, φ
τ
,i+1
φ φi(t) φi+1 t
ti
ti+1
time, t
Fig. 1. Occupant-load ratios in a time period around time ti.
would not exceed 650 ppmv above ambient levels at any time during the occupied period. The existing operating schedule (Schedule 1) is shown in Table 3. This operation schedule assumed full occupant-load during office hours. The ventilation fans would be turned on an hour prior to the nominal office hours and shut down about an hour after the nominal office hours, i.e. from 0800 to 1900 in this office. In actuality, the occupant
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Table 2 Occupant load model parameters of Hong Kong offices Model parameters Occupant load, lnðO1 f Þ Occupant load ratios at a time Occupant load ratios in a time Occupant load ratios in a time Occupant load ratios in a time Model constant at time to, ko,1 Model constant at time to, ko,2 Model constant at time ta, ka,1 Model constant at time ta, ka,2 Model constant at time tb, kb,1 Model constant at time tb, kb,2 Model constant at time tf, kf,1 Model constant at time tf, kf,2
Average (standard deviation) 2.680 (0.426) 0.815 (0.022) 0.950 (0.029) 0.470 (0.040) 0.919 (0.020) 0.636 (0.225) 5.909 (1.022) 0.085 (0.320) 8.200 (2.503) 29.02 (7.127) 117.0 (0.0003) 1.682 (0.387) 5.428 (1.039)
t, /t=10:30,14:30 period soa, /oa period sab, /ab period sbf, /bf
Table 3 Operating schedules of the ventilation system in the example office Time
Number of ventilation fans operated Schedule 1 (existing)
Schedule 2
Schedule 3
Schedule 4
Schedule 5 (proposed)
0 4 4 4 4
0 0 4 4 4
0 0 0 4 4
0 0 0 0 4
0 0 1 3 4
(B) Lunch and afternoon sessions 12:46–13:00 4 13:01–13:15 4 13:16–14:00 4 14:00–17:45 4
3 3 3 4
2 2 2 4
4 4 4 4
3 3 2 4
(C) Evening session 17:46–18:00 18:01–18:15 18:16–18:45 18:46–19:00 After 19:00
4 4 4 4 0
3 3 3 3 0
2 2 2 2 0
4 4 4 4 0
3 2 1 0 0
Total fan-hours (% saving)
44 (0%)
39.5 (10%)
36 (18%)
40 (9%)
35.8 (19%)
(A) Morning session Before 8:00 8:01–8:30 8:31–8:45 8:46–9:00 9:01–12:45
load would vary from time to time and would not be full at any time. It was therefore suggested to optimize the energy use by re-scheduling the operation schedule of the ventilation fan(s) with reference to the designed maximum-CO2 level. A number of proposals for fan-operation optimization in the morning session, the lunch session and the evening session were suggested and are summarized in Table 3 for illustration. Compared with the current operating schedule, Schedule 2 delayed the fan’s starting time by half an hour, switched off one of the four fans during lunch hours and after office hours. Schedule 3 delayed the fan’s starting time by 45 min and switched off two fans during lunch hours and after office hours. Schedule 4 started the fans at the same time as the office hour in the morning and stayed the same throughout the day. Schedule 5 was an example of varying fan-operation for maintaining the probable CO2 levels below a certain limit for this office (see Fig. 2).
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Number of occupants Np (ps)
100
80
60
40
20
Simulated load Observed load
0 6
9
12
15
18
21
Time (hours) Fig. 2. Occupant-load profiles.
4. Results and discussion The performances of the proposed ventilation schedules in Table 3 can be evaluated using the time-variant occupant-load profiles generated for the example office. The probable occupant-load profiles were obtained by simulations of the example office for 800 working days using Eqs. (3)–(5). Using the probable-load profiles, Fig. 3 shows the simulated daily CO2 levels using the operating schedules for the ventilation system during the transient periods, i.e. at (a) beginning of the workday, (b) the lunch period and (c) end of the workday. A new day started with a low level of indoor CO2. The CO2 in the air was probably the residue left behind from the previous day. With the effect of infiltration/exfiltration, the indoor CO2 level was approaching the outdoor ambient CO2 level. Most of the occupants arrived at the office at around 08:30 and the CO2 volume fraction rose correspondingly. At the design occupant-load, with full operation of all 4 ventilation fans, the indoor CO2 would rise to a morning steady-state, which was about 650 ppm above ambient levels as shown in Fig. 3(a). An early start of the ventilation system (Schedule 1) would prevent a sudden rise of the indoor CO2 volume fractions to an extreme peak. Conversely, a late start of the ventilation system would result in failure to keep the morning peak CO2 level below the design CO2 level, i.e. 650 ppm above the ambient levels as stated in the ASHRAE standard [4]. The demands of ventilation could be determined by comparing the morning peak of the indoor CO2 to the design CO2 level. The results showed that satisfactory ventilation would be achieved if the ventilation system started 15 min before the office hours in the example office. Near noon period, some office worker’s leave at various times for lunch, and some stay behind. The results in Fig. 3(b) show that the indoor CO2-volume fractions decrease, due to the decreased load. The indoor CO2 level dropped to a minimum at around 14:00. The ventilation rate could be reduced to achieve energy savings during the lunch break. Compared with the existing operation scheme (Schedule 1), Schedule 2 (shut down one ventilation fan at lunch break) was an example of reducing the fresh-air supply quantity without compromising the indoor environment quality, keeping the indoor CO2 volume fraction less than or equal to 650 ppm above ambient levels. However, further reduction in the ventilation fans operation was not recommended as the peak indoor CO2 volume fractions could be excessive. A combination of the two fan operation schemes would be considered for optimization. Some occupants started to leave at the end of the working day in the example office, i.e. at 17:00, while others would stay after the normal office hours. The indoor CO2-levels decayed slowly as shown in Fig. 3(c). The results showed that the fresh-air rate could be reduced for optimization. Two alternative operation schedules (Schedules 2 and 3) were considered, each with 1 or 2 ventilation fans shut down, and the corresponding simulated CO2 levels were shown in the figure. As indicated, the indoor CO2 levels would be unsatisfactory when more than one ventilation fan was shut down, as in the case of Schedule 3.
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CO2 volume fractions (ppmv)
2400 Schedule 4
2000 1600
Schedule 3 1200 Schedule 2
800 400
Schedule 1
0 6
8
10
12
Time (hours) 1300
CO2 volume fractions (ppmv)
Schedule 3 1100
900
700
Schedule 2
500
Schedule 1
300 12
13
14
15
Time (hours) 1300
CO2 volume fractions (ppmv)
Schedule 3 1100 Schedule 2 900 Schedule 1
700
500
300 17
18
19
20
Time (hours) Fig. 3. Predicted CO2 volume fractions in a working day: (a) beginning of workday, (b) lunch break, (c) end of workday.
After considering the simulated CO2-level results of the alternative operations in Schedules 2–4, an operation schedule (Schedule 5) was suggested to achieve the ventilation objective. This operation schedule was designed to keep the minimum number of ventilation fans in operation, while maintaining the indoor CO2
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1300
CO2 volume fractions (ppmv)
Proposed schedule 1100
Proposed schedule
Proposed schedule
900
700 Existing schedule
500
Existing schedule
300 7
10
13 Time (hours)
16
19
Fig. 4. CO2 volume fractions with example operating schedules of ventilation system in the sample office.
volume-fraction at a level not exceeding 650 ppmv above ambient CO2 levels. A comparison of the two different CO2 levels in the example office, with the ventilation system operating according to the existing schedule and the proposed schedule, is shown in Fig. 4. The probable energy-saving a(%) with the proposed schedule compared with the existing schedule would be approximated by the total ventilation-fan operating hours of the proposed schedule and the existing schedule, where, NF1 and NF0 are the number of fans operating in time i of the proposed schedule and the existing schedule, respectively, P N F1;i a ¼ Pi ð6Þ i N F0;i The total fan operating hours for Schedules 1–5 were determined, with the results summarized in Table 3. Compared with some design norms assuming full occupant-load when designing the office ventilation system, energy savings of the ventilation fan operation with consideration of the occupant load variations in the office could be significant. In this study, using the example office with some occupant-load variation patterns accounted, it was shown that by varying the ventilation fan operating schedule through simple on–off operation of some ventilation fans could result in energy savings up to 19% without any compromise of indoor-air quality. 5. Conclusions Simulations, based on realistic input parameters, could be a very useful technique in evaluating the annual energy consumption and system control strategies for various ventilation systems of air-conditioned offices. In this study, the ventilation demands of an HVAC system due to occupant-load variations were examined against certain IAQ objectives using the mass balance of carbon-dioxide (CO2) volume fractions in an office. The occupant-load variations were reported for the surveyed office during lunch hour and at the beginning and the end of the workday. The variations would have a significant relationship with the ventilation demands of the ventilation system. The transient occupant-load model was used as a basis for evaluating the energy performance of different operating schedules of ventilation systems in typical office-buildings of Hong Kong. The results show that the transient occupant-load in the space is an explanatory parameter in determining the indoor CO2-levels and is therefore an influencing factor on the energy consumption of the ventilation system. Compared with an operating schedule assuming the steady occupant-load in the office during working hours, alternative fan-operating schedules would lead to a reduction in energy consumption of the ventilation fans up to 19%, with the IAQ objective achieved. The model would be a useful source of information for the policymaker to evaluate current operating strategies of ventilation systems in offices. This paper also provides a
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template for evaluating relative energy consumptions of various ventilation strategies for air-conditioned spaces with transient occupant-loads against certain IAQ objectives with a design maximum CO2 volume fraction specified. Acknowledgements The work described in this paper was partially supported by a grant form the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No: PolyU5248/06E, Account Code: BQ01G) and by a grant from The Hong Kong Polytechnic University (Project Account Code: GYE80). References [1] Census and Statistics Department. Hong Kong Energy Statistics, Annual Report 1970–2002. The Government of the Hong Kong Special Administrative Region, China, 2004. [2] Mui KW, Chan WT. A pilot study for the performance of a new demand control ventilation system in Hong Kong. ASCE J Archit Eng 2005;11(3):110–5. [3] ANSI/ASHRAE Standard 62-1981. Design for acceptable indoor-air quality. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers; 1981. [4] ANSI/ASHRAE Standard 62-2001. Design for acceptable indoor-air quality. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers; 2001. [5] ASTM. Standard guide for using indoor carbon-dioxide concentrations to evaluate indoor-air quality and ventilation. D6245 – 98, 2003 [Reapproved 2002]. [6] Jokl MV. Evaluation of indoor-air quality using the decibel concept based on carbon dioxide and TVOC. Build Environ 2000;35(8):677–97. [7] Persily AK. Evaluating building IAQ and ventilation with indoor carbon-dioxide. ASHRAE Trans 1997;103(2):193–203. [8] Mui KW, Wong LT, Law LY. An energy benchmarking model for ventilation systems of air-conditioned offices in sub-tropical climates. Appl Energy 2007;48(1):89–98. [9] Mui KW, Wong LT. Cooling-load calculations in sub-tropical climate. Build Environ 2007;42(7):2498–504. [10] Kaye NB, Hunt GR. Heat source modelling and natural ventilation efficiency. Build Environ 2007;42(4):1624–31. [11] Bartlett FM, Hong HP, Zhou W. Load-factor calibration for the proposed 2005 edition of the National Building Code of Canada: Statistics of loads and load effects. Can J Civil Eng 2003;30(2):429–39. [12] Wong LT, Mui KW. Discharge demand analysis on a drainage stack for residential buildings. Facilities 2006;24(3–4):132–40. [13] Wong LT, Mui KW. Modelling water consumption and flow rates for flushing water systems in high-rise residential buildings in Hong Kong. Build Environ 2007;42(5):2024–34. [14] Wong LT, Cheung TF. Evaluating probable risk of evacuees in institution buildings. Saf Sci 2006;44(2):169–81. [15] Milke JA, Caro TC. A survey of occupant load factors in contemporary office buildings. J Fire Prot Eng 1997;8(4):169–82. [16] Keith DM, Krarti M. Simplified prediction tool for peak occupancy-rate in office buildings. J Illum Eng Soc 1999;28(1):43–56. [17] Wong LT, Mui KW. Modelling transient occupant loads for offices. Archit Sci Rev 2006;49(1):53–8. [18] Hoskins JA. Health effects due to indoor-air pollution. Indoor Built Environ 2003;12(6):427–33. [19] Mo¨hle G, Crump D, Brown V, Hunter C, Squire R, Mann H, et al.. Development and application of a protocol for the assessment of indoor-air quality. Indoor Built Environ 2003;12(3):139–49. [20] Penman JM. An experimental determination of ventilation rate in occupied rooms using atmospheric carbon-dioxide concentrations. Build Environ 1980;15(1):45–7. [21] Roelofsen CPG. The impact of air quality in offices on employee performance – carbon dioxide as a performance indicator for the perceived air quality. Facilities 2003;21(9/10):43–8. [22] Simon M, Vincenc B. The influence of indoor environment in office buildings on their occupants. Build Environ 2004;39(3):289–96. [23] Wong LT, Mui KW, Hui PS. A statistical model for characterizing common air pollutants in air-conditioned offices. Atmos Environ 2006;40(23):4246–57. [24] Frank T. Climate change impacts on building heating-and-cooling energy demand in Switzerland. Energy Build 2005;37(11):1175–85. [25] Keeling CD, Whorf TP. Atmospheric CO2 records from sites in the SIO air sampling network. In: Trends: a compendium of data on global change. Carbon Dioxide Information Analysis Centre (CDIAC), Oak Ridge National Laboratory, US Department of Energy, 2005. [26] Mui KW, Wong LT. Evaluation of a neutral criterion of indoor-air quality for air-conditioned offices in subtropical climates. Build Services Eng Res Technol 2007;28(1):23–33.