Occupancy-based zone-level VAV system control implications on thermal comfort, ventilation, indoor air quality and building energy efficiency

Energy & Buildings 204 (2019) 109473

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Occupancy-based zone-level VAV system control implications on thermal comfort, ventilation, indoor air quality and building energy efficiency Prashant Anand a,∗, Chandra Sekhar a, David Cheong a, Mattheos Santamouris b,a, Sekhar Kondepudi a a b

Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566, Singapore Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia

a r t i c l e

i n f o

Article history: Received 13 June 2019 Revised 28 August 2019 Accepted 28 September 2019 Available online 28 September 2019 Keywords: Occupancy VAV control Ventilation rate IAQ Building energy efficiency Thermal comfort

a b s t r a c t Variable Air Volume (VAV) system serving multiple zones often shows energy wastage issues as it is not able to maintain ventilation requirements efficiently at part-load due to inaccurate assumptions of occupancy and inherent inability to detect and use actual occupancy in control. In this study, the operational data of a typical VAV system has been analysed to study the implications of VAV system on energy efficiency and Indoor Air Quality (IAQ), when controlled using occupancy. Three occupancy-based overlapping operational strategies are proposed: 1. Supply air of the zone is optimized to meet the minimum ventilation requirement and to maintain the zone temperature below 24 °C for both occupied and unoccupied zones; 2. In accordance with the 1st strategy, the supply air of the unoccupied zone, if unoccupied for more than 60 min but less than a day, is further minimized to maintain the zone temperature below 28 °C; and 3. In accordance with the 2nd strategy, no ventilation air is supplied to zones that are unoccupied for the entire day. Based on the outcome of this study, the proposed occupancy-based operational strategies show energy saving potential in the range of 23–34%, 19–38%, 21–31% and 24–34% for classroom, computer room, open office, and closed office zones respectively. The primary contribution of this study is the occupancy-based zone level VAV optimization process and its exploration of possible decision-making tools to save energy. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The Variable Air Volume (VAV) box is a critical component in a VAV system as the amount of air flowing through a VAV box is the main factor affecting the energy efficiency, thermal comfort and Indoor Air Quality (IAQ) [1]. The function of VAV box is to maintain the required airflow rate along with the ventilation requirement based on zone set point temperature [2,3]. If the VAV box does not maintain adequate outdoor air while the amount of supply air reduces, it can cause poor ventilation and IAQ [4]. To avoid such strategies of poor ventilation and IAQ, the airflow rate through a VAV box is normally chosen significantly higher than the minimum required ventilation to prevent poor air mixing but also depends on the diffuser type and its sizing [5,6]. The minimum ventilation requirements for the different type of spaces are specified in standards, such as ASHRAE Standard 62.1

∗

Corresponding author. E-mail address: [email protected] (P. Anand).

https://doi.org/10.1016/j.enbuild.2019.109473 0378-7788/© 2019 Elsevier B.V. All rights reserved.

[7]. Additionally, to avoid energy wastage, ASHRAE Standard 90.1 also limits airflow to the largest of - a. 30% of designed airflow, b. 0.002 m3 /sec for the per unit (m3 ) conditioned floor area of a zone, and c. 142 l/sec [8]. Among the three, 30% or more of designed air flow is very common in practice [1,9]. One possible solution often adopted to minimize the IAQ and energy wastage issues is to fix a range for minimum air flow; however, there are reported cases that this strategy is not always successful due to operational constraints. For example, for a zone with low internal and external heat gain but large area or a zone with sudden increase in occupancy, the calculated air flow rate for cooling may be lower to satisfy ventilation needs [3,10]. Even when this strategy is successful to maintain IAQ, thermal discomfort and energy wastage issues are common due to inaccurate assumption of occupancy [3,10,11]. The zone supply air through VAV box at part loads is often significantly higher than what is required, reducing the zone temperature to lower than zone set-point temperature. For example, a classroom/computer room zone with an occupancy capacity of 40 is often used for meetings with an occupancy of five to ten persons, but the ven-

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P. Anand, C. Sekhar and D. Cheong et al. / Energy & Buildings 204 (2019) 109473

Nomenclature EACMV S Oa Smin QS QAHU Qsl Qopt TZ Tr TO TOC TOfC TS OA SOA (SOA )O (SOA )IL (SOA )min (SOA )pp (SOA )PA Az (S)opt

Energy use for cooling loads Supply air flow rate at operational loads Actual occupancy Supply air flow rate at minimum required ventilation Zone level cooling AHU level cooling Zone level sensible load Optimized cooling load Zone air temperature Return air temperature Outdoor air temperature On-coil air temperature Off-coil air temperature Supply air temperature Outdoor Air Ventilation rate Ventilation required at designed occupancy Ventilation required material emissions Minimum required ventilation as per actual occupancy Per person required ventilation Per unit floor area required ventilation Floor area Optimised supply air flow rate

tilation requirement for the zone is set for the zone is designed for occupancy of 40 and this leads to a much higher supply air flow rate than required. In such cases, the zone often tends to become very cold causing thermal discomfort. Moreover, this practice of providing the supply air flow rate based on design occupancy is continued for zones such as conference room even when they are unoccupied. In comfort air-conditioning, it is not common for these systems to be controlled using occupancy count. The fan of a VAV system is typically operated to control the amount of zonal air flow based on the temperature sensed by the thermostat and design occupancy. In this study, we have proposed and investigated the zonelevel VAV control strategy that uses actual occupancy information to overcome the aforementioned IAQ, thermal comfort and energy efficiency issues. 1.1. Existing occupancy-based VAV control research In the past, many researchers have discussed the possibilities of VAV system related energy savings by controlling the cooling requirement of spaces with an accurate presence/absence information of occupants [12–28]. One research developed a control strategy for VAV boxes by resetting the supply air setpoint based on occupied and non-occupied hours to improve performance for building thermal comfort and energy efficiency [29]. In another study, Stein (2005) suggested a control strategy for spaces which have both cooling and heating loads, proposing dual-maximumcontrol sequence, wherein the supply air setpoint is reset from maximum to minimum when transitioning from cooling to heating before the setpoint is reset from minimum to maximum [30]. Chapman et al. (2006) proposed and patented a control system for managing Air conditioning and Mechanical Ventilation (ACMV) systems based on occupancy by using a programmable thermostat [31]. Another approach postulated that occupancy could be determined by anticipatory programming, based on time of day, zoning and actual sensor based readings [32]. A study by Stanke (2010)

proposed an approach to reset outdoor air-intake for multi-zone AHUs (Air Handling Units) based on occupancy [2]. A study proposed a system which involves a prediction of the presence of occupants based on their past and current behaviour [11]. This prediction of occupancy is then used to infer zone temperature setpoints according to rules specified by the study. It has been found that this control system can save up to 20.3% energy. Additionally, it has been noted that the energy saving potential in an individual office is inversely correlated to its occupancy [11]. Although there have been several designs and control methods proposed so far, most of these have been validated for spaces such as small office which have very low variations in occupancy. There is no reported occupancy based VAV control study for teaching and learning spaces of institutional buildings such as classrooms which have significant variation in occupancy during operational hours and require a more complex control strategy. Occupancy for teaching and learing spaces varies based on various factors such as class and no-class time, semester and semester-break time, examination period, conference period, convocation time, etc. Further, to overcome the complexity of data collection, most of the earlier studies are conducted through energy simulations. Altough a study has validated simulation results with an experimental result, it only used 3-days of experimental data [23,24]. Another limitation with aforesaid study is that the experiments were conducted for a space with just 3 occupants. So, the novel VAV control strategy from the earlier research with a small set of experimental data and consideration of very low occupant density needed to be validated for spaces of large size and with large variation in occupanct density. This research gap has been addressed by selecting a large floor of an institutional building with different space typologies and large occupancy variations for investigation. An extensive 90-day period of actual building operational data has been gathered in this study, from individual zones such as classroom, computer room, open office and a closed office in a floor of an institutional building for analysis [33–37]. So, this study is seen in line as an important extension of and disticnt from previous studies [23,24,37]. Additionally, the outcome of the earlier studies focused on energy efficiency of VAV systems with only a few having discussed its implications over IAQ parameters such as indoor temperature and humidity. In the region of tropical climate, it is extremely important to study the implications of control strategy over indoor humidity. Therefore this study aims to explore the applications of occupancy based VAV control strategies as a possible decisionmaking tool to control the energy as well as IAQ up to zone level. To achive the aforesaid aim, this study investiages the implications of occupancy based VAV control strategy on IAQ and energy efficiency by analyzing the actual zone level occupancy and VAV operational data. The novelty of this study also lies in its scale of zone level data collection and analysis over an extensive 90-day period and with the three propossed occupancy-based VAV control strategies.

2. Research methodology A two-step method is employed to investigate the impact of occupancy-based control of a VAV system serving multiple zones (Fig. 1). Firstly, the zone and AHU level time series data of temperature, humidity, airflow rate, ventilation rate, and cooling load are collected. Further, the collected data is analysed for individual zones based on three control strategies discussed in the following subsection to identify the implications of occupancy based VAV control over energy efficiency and IAQ during both occupied and unoccupied hours.

P. Anand, C. Sekhar and D. Cheong et al. / Energy & Buildings 204 (2019) 109473

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Fig. 1. Research methodology.

VAV control strategies Strategy 1: Maintain optimized ventilation for both occupied and unoccupied zones The purpose of this strategy is to avoid the overventilation issues with conventional VAV operation. In this strategy, the minimum required ventilation rates of individual zones are calculated using the actual occupancy and material emission requirements of individual zones. Zone temperature is maintained below 24 °C for both occupied and unoccupied zones as per actual operational setpoint. This strategy addresses the current inefficiency of VAV system as it fails to supply optimum ventilation due to lack of realtime occupancy information. Strategy 2: Maintain optimized ventilation for occupied zone only In addition to optimizing the ventilation of occupied zones, the purpose of this strategy is to save energy by further reducing the ventilation of unoccupied zones. For unoccupied zones, minimum ventilation is required to dilute chemical contaminants emitted from furnishing [38]. However, this approach is only necessary for spaces where material emission is significant and hazardous to human health. The material emissions for zones like classrooms where the major emissions are from furniture are mostly nonhazardous and so the ventilation can be further minimised during unoccupied hours. Hence, along with strategy 1, the possibility of further reduction in supply air has been investigated in this strategy by reducing the ventilation of zones unoccupied for more

than an hour and less than a day. However, reduction in supply air from the minimum required to maintain ventilation as per ASHRAE standard 62.1 may elevate zone temperature, leading to thermal discomfort for the occupant upon entering the space. This issue has been solved by limiting the reduction in supply air of the unoccupied zone in such a way that the zone temperature does not exceed 28 °C (near the boundary of upper acceptable temperature). Strategy 3: Discontinue ventilation of unoccupied zone In this strategy, the energy-saving possibilities have been further extended by avoiding the air-conditioning for zones which are unoccupied for more than a day. So, following strategy 2, ventilation of such zones is completely discontinued in this strategy. These three strategies are summarised in Table 1. 2.1. Data collection In this study, actual building operational data has been collected from an entire floor of an institutional building which has five types of spaces: (a) Classrooms (3 in number); (b) Computer rooms (2 in number); (c) Closed offices (10 in number); (d) Open offices (2 in number); and (e) Corridors. The maximum design occupancy for each computer room, classroom, closed office and open office is 25, 30, 3 and 31 respectively (See Fig. 2). Time-series data of the environmental and occupancy related parameters listed in Fig. 1, with 5-minute intervals, is under continuous monitoring for the floor studied. To ensure data quality,

Table 1 Summary of operational strategies. Strategy

Applicable for zone which is

Continuous unoccupied hours

Zone temperature constraint

1

Occupied Unoccupied

Not applicable < 60 min

< 24 °C < 24 °C

2

Occupied Unoccupied

Not applicable >= 60 min
< 24 °C < 28 °C

3

Occupied Unoccupied Unoccupied

Not applicable >= 60 min = a day

< 24 °C < 28 °C No supply air

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P. Anand, C. Sekhar and D. Cheong et al. / Energy & Buildings 204 (2019) 109473

Fig. 2. Layout of floor used in the study.

control measures have been taken during both collection and analysis of data. It has been ensured that the installed sensors are calibrated during data collection. Additionally, a systematic data cleaning process using MATLAB 2018 has been performed for the removal of outliers and imputation of missing data values. Thus, this study has used a robust data of a period of total 90 days for the analysis, distributed across two continuous semesters (30 days of active teaching period, followed by 30 days during semester break and then 30 days again with active teaching). The data has been collected for 24 h a day, in which the normal building operational hours are between 9:00 AM and 10:00 PM. The ACMV system of the floor studied is operated by two Air Handling Units (AHUs) of 239 kW combined capacities, which is about 50% surplus of the actual maximum operational load during occupied hours. However, this surplus capacity decreases to only 39% during the morning hour loads encountered in the purging process of ACMV systems. The purging process in an ACMV system is used to remove the overnight air from the zones and is a common practice in Singapore to replace the deteriorated overnight indoor air with outdoor air to improve IAQ every morning prior to normal operational hours. For the detection of occupancy, camera-based sensors are installed in every zone of the selected floor to capture the number of occupants in the zone. Additionally, for the environmental data, such as air temperature and relative humidity, environmental sensors are installed in every zone. The camera-based occupancy

counting technology shows greater than 95% accuracy [39,40]. The technology used in this study usually fails only for the cases when two or more occupants are sitting, standing or moving in such a way that they overlap each other. The overlapping scenario of occupancy usually occurs for a very short time especially just before and just after class hours. This slight biasness in occupancy data has been ignored in this study on the assumption that it would not significantly influence the results and analysis of this study. On-coil, off-coil land zone-level air side data is obtained from the environmental sensors installed in every zone and Building Management System (BMS) database. Further, the cooling load data is obtained using the BTU meter installed at the AHU. The precision of data used in this study is reported in Table 2 and a detailed description of data collection process for the floor under investigation is discussed in [37,40]. 2.2. Data analysis 2.2.1. Optimization process of conventional VAV operation The analysis for optimization of VAV operation based on occupancy information is performed in a six-step iterative process: Step 1: Calculation of minimum required ventilation rate In a conventional VAV operation, the ventilation required for any cooling load is calculated as a sum of ventilation required for occupants at design occupancy and for material emissions removal,

Table 2 Details of installed sensors. Accuracy Sensor type

Technology used

BTU Meter Energy Meter

Electromagnetic Flowmeter Energetix (copyright of GreenKoncepts) Raspberry Pi

Temperature Relative Humidity (%) Occupancy

Camera + Deep learning based Xintelligence (copyright of Xjera)

As per product sheet

As verified

± 0.5% NA

Physical verification not possible ± 1.2%

± 0.2 °C ± 1.8% > 88% for the raw data & >95% for processed data

± 0.8 °C ± 2.0% >95%

P. Anand, C. Sekhar and D. Cheong et al. / Energy & Buildings 204 (2019) 109473

as shown in Eq. (1).

SOA = (SOA )IL + (SOA )O

(1)

Here, SOA - Total ventilation required (SOA )IL – Ventilation required for material emissions (SOA )O – Ventilation required by occupants at design occupancy The ventilation rate can be adjusted as per actual occupancy using Eq. (2) to calculate the minimum ventilation required (SOA )min of a specific zone.

(SOA )min = (SOA )pp · Oa + (SOA )IL W here, (SOA )IL = (SOA )PA · Az

The minimum required ventilation obtained from step 1 is used to identify the minimum zone supply air flow rate (S)min using Eq. (3).

(S )min =

(3)

OA%

Here OA% is the percentage of outdoor air in total supply air. Step 3: Calculation of zone temperature using supply air flow rate obtained from step 2 Based on minimum zone level supply air flow rate, the zone set point temperature (Tz ) is obtained using Eq. (4). The off-coil temperature (TS ) air is considered similar to the first-time step (t = i) of a daily actual operational data. So, the zone set point temperature is allowed to increase or decrease based on occupancy level to balance the relationship in Eq. (4).

(Tz )t=i =

zone load + (Ts )t=i S ( )min · ρ · Cp

(4)

Where,

zone load = Qi + Qe and t is the time step. Step 4: Optimization of supply air flow rate based on zone temperature constraint There may be a case when (S)min may not be able to satisfy the zone thermal comfort requirement. In view of this, zonal supply air flow rate is optimized based on the zone temperature constraints described in Eq. (5) in which (S)opt is the optimized zone supply air flow rate and Tmax is the maximum allowable zone temperature. The value of Tmax is identified based on different operational strategies discussed listed in Table 1.

(S )opt =

⎧ ⎨(S )min , TZ ≤ Tmax −

⎩ zone load

ρ Cp (Tmax −Ts ) ,

(Toc )t=i+1 = (1 − OA%/100 ) · (Tr )t=i+1 + (OA%/100) · (To )t=i+1 (6) Where,



n i=1 TZ (i )

(2)

Step 2: Calculation of supply air flow rate based on minimum required ventilation

(SOA )min

A change in zone air temperature (TZ )t =i+1 subsequently results in a change in return air temperature (Tr )t=i+1 and on-coil temperature (Toc )t=i+1 for next time step (t = i + 1). It is to be noted that return air temperature of a zone is considered equal to the zone air temperature. So, based on (TZ )t= i obtained from step 3 and 4, a revised on-coil air temperature is calculated for next time step using Eq. (6) in which ‘To ’ is the outdoor air temperature.

(Tr )t=i+1 =

In the above Eqn, (SOA )pp is minimum ventilation requirement per person (0.0025 m3 /s per person) (ASHRAE, 2016); (SOA )PA is ventilation required per unit floor area as specified by ASHRAE standard 62.1 (ASHRAE, 2016); Oa is actual occupancy and Az is the floor area.

5

() · (S )opt z i

t=i

(S )opt

Step 6: Calculation of revised supply air temperature based on changed zone temperature A change in on-coil air temperature (TOC )t=i+1 subsequently results in a change in off-coil temperature (TOFC )t=i+1 if the difference between on-coil and off-coil air temperature is kept constant. As one of the purposes of this study is to identify the change in energy consumption by changing supply air flow rate, the temperature difference between on-coil and off-coil air (T) is kept constant at the value of actual operation. So, the revised supply air temperature (TS )t=i+1 for next time step (t = i + 1) can be computed using Eq. (7).

(Ts )t=i+1 = (Toc )t=i+1 + T

(7)

The optimization process from steps 3 to 6 is a discrete time closed-loop iterative process as illustrated in Fig. 3. For example, at any time-step of t = i, zone level supply air quantity is obtained based on the minimum required ventilation using actual occupancy. Based on this supply air, zone level temperature is calculated. If zone temperature is less than 24° or 28 °C based on adopted strategy, there will be no change in supply air, but if the zone temperature exceeds this upper threshold of zone temperature (24° or 28 °C), supply air will be revised to reduce the zone temperature to 24° or 28 °C. Furthermore, based on this revised zone temperature, which is also the return air temperature, revised on-coil air temperature is obtained at time t = i + 1. This oncoil temperature is then used to estimate the revised off-coil air temperature at time t = i + 1 by keeping the T between on-coil and off-coil air the same, as in actual operation. For this revised off-coil air temperature, minimum required ventilation from step1 and temperature constraints are now inputs for the optimization of zone level supply air at time step of t = i + 1. This optimization process will be repeated on a daily basis, considering the actual operation values at the start of VAV operation as initial values (t = 0) for the iterative process. 2.2.2. Energy use implications of occupancy-based VAV control To identify the energy saving for occupancy based VAV control, firstly the occupancy based optimized zone level cooling load (Qopt ) is calculated using Eq. (8). Then, the percentage energy saving is calculated based on the difference of Qopt from the actual supplied zonal level cooling load (Qs ) using Eq. (9). Here, the zone level Qs data is obtained based on the actual zone level supply air rate (S), off-coil level supply air rate (Soc ) and AHU level cooling load (QAHU ) data using Eq. (10).

Qopt = (S )opt · c · ρ · T (5)

TZ > Tmax

Step 5: Calculation of revised on-coil air temperature based on revised zone temperature

Energy saving % =

(8)

(Qs − Qopt ) Qs

× 100

(9)

Where,

Qs =

S × QAHU Soc

(10)

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Fig. 3. Illustration of occupancy-based optimization process.

3. Results 3.1. Energy use flow To understand the ACMV energy (EACMV ) usage in buildings, it is important to first study the distribution of EACMV in different zones. Such analysis is useful to compare the cooling load requirements of different types of zones. In view of this, distribution of floor level EACMV with respect to each zone type is presented in

Fig. 4. Out of the total EACMV , 70% is used during semester and 30% is used during semester-break period. It is further distributed into individual zones with a distribution of 24.3%, 17.7%, 20.0%, 15.0% and 23.0% for classroom, computer room, open office, closed office and corridor respectively. Overall, it can be observed from Fig. 4 that in the floor under study, the teaching and learning zones such as classroom and computer room together, are major components of total EACMV . Although building operational hours are from 9:00 AM to 10:00 PM,

Fig. 4. Distribution of ACMV energy for different zones.

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Fig. 7. Box-plot of zone level minimum required ventilation rate. Fig. 5. Daily ACMV system energy use profile.

the operational hours of ACMV system are normally fixed from 8:00 AM to 9:45 PM as shown in Fig. 5. Since ACMV system is operated within this fixed operating period irrespective of occupancy and holiday information, the zones are air conditioned even during unoccupied hours. It has also been noticed that during one rainy day, EACMV load profile showed slightly lower cooling requirements as compared to the normal day. This means that the outdoor air during rainy day was much cooler as compared to normal day. 3.2. Energy distribution among occupied and unoccupied hours The zone level distribution of EACMV is further divided into occupied and unoccupied hours as shown in Fig. 6. For EACMV study, unoccupied hours are defined as hours with zero occupancy while occupied hours are defined as hours with non-zero occupancy. In the case of the corridor, total operational hours are considered as occupied, irrespective of occupancy because these kinds of spaces have only transitional occupancy. Controlling VAV terminal boxes based on occupancy information for this transitionally occupied space may not be a practical solution, and hence, it has been excluded from further analysis. Unoccupied-hour EACMV ranges from 24% for classroom to 73% for computer room. The Computer room has the maximum percentage of EACMV during unoccupied hours because it was not operational for most of the time during semester break. Open office has a major portion of unoccupied-hour EACMV as it is operational from 9:00 AM to 6:00 PM only, while the ACMV system operates from 8:00 AM to 9:45 PM. Similarly, closed office also has 57%

Fig. 6. Energy use distribution during occupied and unoccupied hours.

unoccupied-hour EACMV which can be attributed to the fact that its occupants (faculty) are most of the time out of office for lectures, seminars or meetings. Additionally, it is found that ACMV systems are operational even on weekends and public holidays, contributing to high energy wastage. Up to 30% of annual EACMV can be easily saved just by switching-off the ACMV system on non-working days. Furthermore, on working days as well, there is significant amount of energy consumption during unoccupied hours, which provides the opportunity to optimize the ACMV system operation based on occupancy information. 3.3. Conventional vs occupancy-based VAV control 3.3.1. Zone level actual and minimum required ventilation rate Fig. 7 compares (SOA ) and (SOA )min for the different zones. It can be seen that the values of (SOA ) are higher than (SOA )min for each zone. The median values of (SOA )min are 0.10, 0.09, 0.10 and 0.08 m3 /sec for classroom, computer room, open office and closed office respectively [37]. For the same zones, (SOA ) are 0.16, 0.14, 0.14 and 0.12 cum/sec respectively [37]. In addition, based on the size of box plots, closed office ventilation rate data indicates less variation in ventilation rate as compared to other zones. The closed offices are usually occupied by a single occupant, causing less variation in ventilation requirements during occupied hours [37]. Computer room and classroom have a variation of occupancy between 1 and 45, resulting in a higher variation of ventilation rate than that of open and closed offices [37]. 3.3.2. Optimized supply air flow conditions From Fig. 8 which compares the supply air temperature at proposed strategies, it is evident that off-coil air temperature varies between 11 °C and 18 °C. Maximum variation in supply air temperature is seen for Strategy 2 as it allows the ACMV system to operate at two different upper temperature limits (24 °C and 28 °C) based on the occupancy status of the zones. In actual operation, supply air was mostly maintained close to 13 °C. Hence, it is important to explore the variation of optimized off-coil air conditions at various occupancy levels. In view of this and for the ease of explanation, one day data of actual and optimized operational data of temperature and ventilation rate of off-coil supply air have been compared in a time series plot. Fig. 9 compares the actual supply air temperature with optimized off-coil air temperature for strategies 1 and 2. The chart

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Fig. 8. Optimized off-coil air temperature.

shows that with occupancy-based strategies, off-coil air temperature decreases with an increase in occupancy and increases with a decrease in occupancy. The increase in off-coil air temperature is higher for strategy 2 as compared to strategy 1. Strategy 2 allows zone temperature to increase to 28 °C for unoccupied zones whereas strategy 1 allows the temperature to increase to 24 °C. It is to be noted that the occupancy illustrated in Fig. 9 is total occupancy including all zones; however, on the same day, some of the zones were unoccupied for a few hours where strategy 2 is implemented. Fig. 10 compares the actual off-coil ventilation rate with optimized off-coil ventilation rates for strategies 1 and 2. The actual ventilation rate trend has no relationship with actual occupancy. However, in the case of suggested occupancy-based strategies, ventilation rate follows occupancy trend, i.e. an increase in occupancy shows an increase in ventilation rate and vice versa. 3.4. Implications of occupancy-based VAV control 3.4.1. IAQ It can be seen from Fig. 11 that the actual ventilation supplied to all zones is mostly higher than the minimum required venti-

lation, except for classroom and computer room during evening class hours (Highlighted as mark ‘B’ in figure). The actual ventilation supplied to classroom and computer room during evening class hours (7:00 PM to 10:00 PM) when occupancy is very high, does not meet the required minimum ventilation. This poor IAQ condition during evening classes could be attributed to the open offices covering a large area of the floor not being operational in the night. This leads to the reduction of internal load, which consequently may lead to the reduction in the total air supplied to the zone. Similar phenomena have been mentioned by Chen and Demster [4]. This is quite a common issue for AHUs which have a fixed outdoor air damper position. It can be seen from Fig. 11 that the quantity of actual ventilation is reduced for all the zones despite the fact that during evening hours, it is only the classroom, computer room and a few closed offices that are operational. Furthermore, it is also evident that in the case of occupancy based optimized ventilation, none of the zones face ventilation issues during occupied hours. Strategy 1 maintains proper ventilation even during unoccupied hours, but strategy 2 has declined in air quality when a zone is unoccupied for more than an hour (indicated as A in Figs. 11). This slight decline in ventilation for strategy 2 during unoccupied hours is mainly reported for open offices and closed offices. 3.4.2. Thermal comfort The temperature and relative humidity data of actual and occupancy based VAV system operation are plotted on a psychrometric chart. This has been done to discuss the implications of occupancy based VAV control on thermal comfort. These psychrometric charts are prepared using MATLAB 2018b. The constraints on the zone temperature used in this study during the occupied and unoccupied hours for Strategies 1 and 2 are shown as red and blue squares respectively in Fig. 12. The constraint on zone temperature and relative humidity defined by the boundary of the red and blue squares are within ASHRAE specifications, provided that the air speed in the zone is less than 0.2 m/s and the occupants are wearing clothing having thermal resistance between 0.0775 m2 K/W and 0.155 m2 K/W while performing sedentary tasks. Fig. 12 shows that the actual temperature of classroom is as low as 19 °C. Similarly, it can be seen that zone temperature reaches as low as 21 °C in the computer room, open office and closed offices zones. However, in the case of occupancy-based control, the zone temperature is always maintained above 21 °C. In addition, a

Fig. 9. Optimized off-coil air temperature.

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Fig. 10. Optimized off-coil ventilation rate.

Fig. 11. Ventilation for different zones.

significant amount of data points outside the red boundary when temperature is above 24 °C, is an indicator for unoccupied hours where strategy 2 is being applied. While most of the data points of strategy 1 are below the absolute humidity level of 12 g/kg, a significant amount of data points from strategy 2 belonging to unoccupied hours have absolute humidity up to 15 g/kg. 3.4.2.1. Temperature. Fig. 13 shows the comparison of actual zone temperature against optimized zone temperature at various cooling loads for strategies 1 and 2. It can be seen from Fig. 13, that the actual zone temperature is mostly lower than 24 °C during occupied hours, except for evening classes, when the temperature of classroom and computer room tends to reach 25 °C (indicated as B in Figures). As discussed earlier, it can be seen that in conventional VAV operation, the tem-

perature of classroom during unoccupied hours reaches as low as 19 °C, while for all the other zones it reaches close to 21 °C. The additional drop in classroom zone temperature during unoccupied hours can be explained by the fact that classroom zone has larger area coverage than other zones which leads to higher supply air through the VAV box serving classroom zone than other zones based on conventional approach of calculating supply air flow rate. This is a typical case of overcooling when a zone has low internal and external heat gain but a large floor area. Similarly, the higher temperature during class hours could be due to a sudden increase in occupancy for which the calculated supply air flow rate may be too low. 3.4.2.2. Relative humidity. Similar to zone temperature, a comparison of relative humidity of zones with varying cooling loads for strategies 1 and 2 are performed for actual relative humidity.

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Fig. 12. Zone conditions of actual vs occupancy-based VAV control.

It is evident from Fig. 14 that the actual zone humidity tends to go as high as 68% during unoccupied hours and 66% during occupied hours. In actual operation, relative humidity decreases with an increase in occupancy because zone temperature increases with an increase in occupancy. However, with occupancy-based control, humidity increases when the occupancy status of zone changes from unoccupied to occupied hours. This increase in humidity can be attributed to the fact that temperature of the zone decreases from unoccupied to occupied hours in strategies 1 and 2. The lowest relative humidity during unoccupied hours is noticed for strategy 2 as it allows zone temperature to increase up to 28 °C. 3.4.3. Energy efficiency It can be seen in Fig. 15 that up to 23%, 19%, 21% and 24% of EACMV savings are annually possible for classroom, computer room, open office and closed office zone respectively by implementing strategy 1. If strategy 2 is also implemented, there is a further reduction in EACMV consumption by an additional 6%, 9%, 10% and 10% for classroom, computer room, open office and closed office zone respectively. Furthermore, additional 5% and 10% of energy savings are possible using strategy 3 as well for classroom and computer room respectively, which are unoccupied for a number

of days during semester-break. Overall, the highest percentage of EACMV saving (38%) is possible for the computer room, due to the fact that it is mostly not in operation during semester break. Strategy 3 has not been investigated for open offices and closed offices as these spaces have been considered operational throughout the year. However, there is a possibility to further explore strategy 3 for closed offices for the cases when the occupants of open offices are on leave. 4. Discussion Based on the study outcomes, there is a potential of energy saving without compromising IAQ using strategy 1 for other spaces which have high variation in occupancy during occupied hours. It would be interesting to apply these strategies for applications where occupancy fluctuates significantly, such as food courts and canteens. Further, strategy 2 shows its potential for additional energy savings for spaces which are unoccupied for a shorter duration with no/little compromise with IAQ. Other than the spaces investigated in this study, strategy 2 could be further investigated for shared and coworking spaces. Although inclusion of strategy 3 shows the highest energy savings, it is only applicable for spaces for which there is a prior (day ahead) information that the space is going to be unoccupied for more than a day. Moreover, the

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Fig. 13. Temperature for different zones.

Fig. 14. Relative humidity for different zones.

outcome of this study can be utilized in real time control of VAV system if there is a provision of real-time monitoring of occupancy, plug load, lighting load along with zone level VAV airflow control actuators. Fig. 16 presents the methodology to monitor and control ACMV energy consumption for individual zones in real time based on actual zonal occupancy information. The methodology has mainly two steps: (a) Real-time monitoring and collection of occupancy data and (b) Use of real time occupancy data for occupancy-based VAV system control.

The real-time monitoring of occupancy for individual zones can be used to calculate real-time heat gain by occupants and minimum required ventilation for the ACMV control. The real-time heat gain by other sources such as plug, and lighting loads can be obtained from energy meters. Additionally, the heat gain for external sources can be easily simulated in real-time using the energy simulations software such as EnergyPlus. Based on these realtime heat loads, and zone temperature constraints, the amount of supply air flow rate can be optimized to maintain the ventilation amount close to the minimum required ventilation. Furthermore,

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Fig. 15. Estimated annual ACMV energy savings.

based on the optimized supply air flow rate, the temperature of off-coil air can be controlled in such a way that it always satisfies the zone temperature constraint. Further, for the implementation of occupancy-based building energy use system monitoring and control, BMS facilities around the world do not yet have any occupancy-based building energy use control infrastructure. Thus, the main barriers in the implementation of occupancy-based building energy use system control could be cost implications as it requires a detailed network of sensors and actuators from AHU to zone level. However, the cost implications may not be a challenge in future as the price of sensors

is decreasing with time. The increasing demand of these sensors and highly competitive market of sensor manufactures, in addition to the availability of cheaper and innovative technologies, could be the reasons of declining prices of sensors. Additionally, before the implementation of any control strategy, there is a need to understand the preference of occupants. For example, in the case of classroom lighting load saving, there may be a situation when students prefer private space during study, but the strategy suggested in this study tends to make them concentrate in one area in the classroom. The validation of the proposed method with real-time occupancy-based building system control is beyond the scope of this study because of the limitation associated with the existing research infrastructure and time. So, this study is limited to the assessment of available data of an existing building’s VAV system operation to identify the energy efficiency and IAQ implications, if it is controlled using occupancy information. In this study, time lag required by VAV system to maintain the desired zone temperature is considered zero, hence the performance of occupancy based VAV control reported in this study may vary in the real-time operation. In practical operation, VAV system may take some time to achieve the desired zone temperature based on the size of supply air duct and the time required by building management system to analyse and respond to the real-time occupancy information. So, the energy savings reported in this study are purely data-driven based and the quantity of the same can vary in real-time operation based on sensor settings and system performances. Notwithstanding, the operational strategies proposed in this study is considered robust. Further to deal with the privacy and security aspects of occupancy

Fig. 16. Occupancy-based control methodology for cooling load.

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detection using a camera, this study, has been conducted in accordance with Institutional Review Board (IRB) approved guidelines. As per IRB guidelines, the occupancy data inside a space have been detected and processed without disclosing an individual’s identity. 5. Conclusions In a conventional VAV based operation, zone level set-point temperatures have to be maintained irrespective of occupancy. In cases of low or no occupancies, the absence of occupancy information often leads to issues related to thermal comfort, IAQ and energy wastage. If real-time occupancy information is available, the supply air flow can be optimized by allowing the temperature of a zone to increase or decrease according to actual occupancy-based minimum required ventilation and zone temperature constraints. The result of this study shows that the actual supply ventilation is mostly higher than the minimum required except during the evening classes in the classroom and computer room. Energy savings are possible by reducing the ventilation to an optimal level based on real-time occupancy information. It has been noticed that the low occupancy spaces such as closed offices, have less variation in ventilation requirements during occupied hours. However, higher occupancy spaces such as classroom and computer room, have a large variation of ventilation rate during occupied hours. Overall, it has been found that in the spaces with fluctuating occupancy, a significant amount of energy can be saved just by maintaining the zone ventilation in proportion to occupancy. Additionally, the proposed three occupancy-based VAV control strategies can establish better synergy between IAQ, thermal comfort and energy savings than conventional temperature-based control. Strategy 1 could be applicable during both occupied and unoccupied hours for space which has high variation in occupancy without compromising IAQ and thermal comfort. Strategy 2 is only applicable during unoccupied hours for spaces which are unoccupied for a shorter duration and strategy 3 is applicable for the spaces which are unoccupied for more than a day and where it is permissible to completely discontinue ventilation. Declaration of Competing Interest The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patentlicensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Acknowledgement The study was supported by the National University of Singapore under CiBEST (BEE Hub) and the data collection methodology is approved by Institutional Review Board with NUS-IRB Ref No.: S-18-001. References [1] Y.-H. Cho, M. Liu, Minimum airflow reset of single duct VAV terminal boxes, Build. Environ 44 (2009) 1876–1885, doi:10.1016/j.buildenv.2009.01.001. [2] D. Stanke, Dynamic reset for multiple-zone systems, ASHRAE J 52 (2010) 22– 35 www.ashrae.org. (accessed November 12, 2018). [3] G. Liu, M. Brambley, Occupancy based control strategy for Variable Air Volume (VAV) Terminal Box Systems (ML-11-C030), ASHRAE Trans. (2011). [4] S. Chen, S. Demster., Variable Air Volume Systems for Environmental Quality, McGraw-Hill, 1995. [5] S.T. Taylor, J. Stein, Sizing VAV boxes, ASHRAE J. 46 (2004) 30-32+34+36 http://www.taylor-engineering.com/Websites/taylorengineering/articles/ ASHRAE_Journal_- _Sizing_VAV_Boxes.pdf . (accessed November 12, 2018).

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