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Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms

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Namchul Seong at Kangwon National University
Namchul Seong
Jee-Heon Kim
Jee-Heon Kim
Jee-Heon Kim
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Wonchang Choi
Wonchang Choi
Wonchang Choi
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Abstract and Figures

This study is aimed at developing a real-time optimal control strategy for variable air volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using genetic algorithms and a simulated large-scale office building. The two selected control variables are the settings for the supply air temperature and the duct static pressure to provide optimal control for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal control settings for each control variable. The proposed optimal control conditions were evaluated according to the total energy consumption of the HVAC system based on its component parts (fan, chiller, and cold-water pump). The results confirm that the supply air temperature and duct static pressure change according to the cooling load of the simulated building. Using the proposed optimal control variables, the total energy consumption of the building was reduced up to 5.72% compared to under ‘normal’ settings and conditions.
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sustainability
Article
Optimal Control Strategy for Variable Air Volume
Air-Conditioning Systems Using Genetic Algorithms
Nam-Chul Seong 1, Jee-Heon Kim 1and Wonchang Choi 2,*
1
Eco-System Research Center, Gachon University, Seongnam 13120, Korea; inamchul@gachon.ac.kr (N.-C.S.);
jlaw80@naver.com (J.-H.K.)
2Department of Architectural Engineering, Gachon University, Seongnam 13120, Korea
*Correspondence: wchoi@gachon.ac.kr; Tel.: +82-31-750-5335
Received: 3 September 2019; Accepted: 18 September 2019; Published: 19 September 2019


Abstract:
This study is aimed at developing a real-time optimal control strategy for variable air
volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using
genetic algorithms and a simulated large-scale office building. The two selected control variables are
the settings for the supply air temperature and the duct static pressure to provide optimal control
for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal
control settings for each control variable. The proposed optimal control conditions were evaluated
according to the total energy consumption of the HVAC system based on its component parts (fan,
chiller, and cold-water pump). The results confirm that the supply air temperature and duct static
pressure change according to the cooling load of the simulated building. Using the proposed optimal
control variables, the total energy consumption of the building was reduced up to 5.72% compared to
under ‘normal’ settings and conditions.
Keywords:
heating, ventilation and air-conditioning (HVAC) system; variable air volume (VAV);
optimization; genetic algorithm
1. Introduction
Heating, ventilation, and air-conditioning (HVAC) systems constitute a significant portion of the
energy consumption of many buildings [
1
–
3
]. Researchers have tried to determine a methodology
to reduce such HVAC energy consumption [
2
,
4
] and, among the developed methodologies, genetic
algorithms (GAs) are widely used in various fields and are known to be suitable for solving complex
optimization problems, especially when large amounts of data and parameters are involved [
5
,
6
].
Therefore, GAs (a type of machine learning technique) are applicable for the optimization of complex
configurations of systems such as buildings [
7
]. Extensive research has been conducted to optimize the
thermal performance of buildings and reduce energy consumption, particularly that of the building’s
HVAC system [
8
]. Recent studies have shown that optimization methods that use GAs can save energy
in HVAC systems and improve energy efficiency [
9
–
11
]. Researchers have carried out analyses of the
changes in the energy consumption of HVAC systems with respect to building design parameters [
9
]
and have optimized HVAC system design based on simulations [10].
The performance of buildings and their HVAC systems is influenced in real time due to factors
such as temperature and humidity of the outside air, operation modes and patterns, and others. In order
to implement efficient operations and effectively control a building’s HVAC system, the HVAC system
must be operated and controlled by optimal control variables (settings) in real time that correspond to
the changes in load usage according to the external environment. Preferably, such optimal operation
and control can be implemented without additional costs for updating the system [12].
Sustainability 2019,11, 5122; doi:10.3390/su11185122 www.mdpi.com/journal/sustainability
Sustainability 2019,11, 5122 2 of 12
The typical local controls in a valuable air volume (VAV) air-conditioning system rely on static
pressure control, supply air temperature, and outdoor air flow [
13
–
15
]. These local controls have an
effect on the indoor comfort and energy consumption in the system. The appropriate controls can be
achieved by adopting indoor loads and outdoor conditions in real time. Local controls in a VAV system
have traditionally been implemented using controllers with values derived from traditional control
strategies such as setpoint reset, proportional integral derivative (PID), and model predictive control
(MPC) [16–18].
HVAC systems are comprised of many components and subsystems (e.g., fans, chillers, pumps,
ductwork, pipes, heat exchangers, etc.). In this research, an office building was used as the reference
building in which the HVAC system was modeled and simulated. The optimal control variables were
derived from genetic algorithms (GAs) and are proposed to mitigate energy consumption and optimize
the performance of HVAC systems in large-scale office buildings. The calculated optimal control
variables/settings were input to a variable air volume (VAV) air-conditioning system at one-hour
intervals. GAs were employed to analyze the changes in the calculated optimal control variables
which, in turn, were used to operate the modeled system. Using this process, energy savings were
calculated and compared with the energy consumption under previous ‘normal’ (compared to ‘optimal’)
operating conditions.
2. Generation of Simulated Reference Building and Description of VAV System
Among the standard buildings found in the Commercial Prototype Building Models proposed
by the United States Department of Energy’s Building Energy Codes Program [
19
,
20
] and
ANSI/ASHRAE/IES Standard 90.1 [
21
] for standardized energy assessment, the category of Large Office
Buildings [
22
] was selected as the building type to generate the required input data for this study.
The input values for the reference building were modified to reflect the usage profile of a large office
building in Korea [
23
]. To fit the simulation program, weather information was obtained by modifying
test reference year (TRY) data, which are the standard weather data for the Seoul area. Table 1presents
the main boundary conditions that were used in the proposed simulation model to acquire data.
Table 1.
Simulation Conditions for Reference Large-Scale Office Building. HVAC: heating, ventilation,
and air-conditioning.
Component Features
Weather data and site location Test reference year (TRY) Seoul
(latitude: 37.57◦N, longitude: 126.97◦E)
Building type Large-scale office building
Total building area (m2)46,320
Hours simulated (hour) 8760
Envelope insulation (m2K/W) External wall 0.35, roof 0.213, external window 1.5
Window-to-wall ratio (%) 40
Setting (◦C) Cooling 26, heating 20
Internal gain Lighting 10.76 (W/m2), people 18.58 (m2/person),
plug and process 10.76 (W/m2)
HVAC sizing Autocalculated (software to be determined)
HVAC operation schedule 7:00–18:00
The building and its HVAC system were simulated using EnergyPlus version 8.9.0 (U.S. Department
of Energy, Washington, DC, USA) [
24
]. The outputs, which include energy consumption, were
generated from EnergyPlus and include quantities for flow, temperature, and pressure at each node
in the building’s system. The HVAC system modeled in this study has an air handling unit that
Sustainability 2019,11, 5122 3 of 12
provides a VAV system to each room and a freezer that can be used also as a heat source in cold weather.
Numerous controllable settings are required to manage a VAV system, from heating and cooling
temperature settings and minimum airflow rate settings at the zone level to the minimum outside
airflow, supply air temperature, and duct static pressure settings at the system level [
25
]. Among the
various controllable variables in an HVAC system, the settings for supply air temperature and duct
static pressure are selected specially for the study as the control variables in the VAV air-conditioning
system. Figure 1describes a typical VAV HVAC system that includes the control setting locations for
the supply air temperature and duct static pressure.
Sustainability 2019, 11, x FOR PEER REVIEW 3 of 11
consumption, were generated from EnergyPlus and include quantities for flow, temperature, and
pressure at each node in the building’s system. The HVAC system modeled in this study has an air
handling unit that provides a VAV system to each room and a freezer that can be used also as a
heat source in cold weather. Numerous controllable settings are required to manage a VAV system,
from heating and cooling temperature settings and minimum airflow rate settings at the zone level
to the minimum outside airflow, supply air temperature, and duct static pressure settings at the
system level [25]. Among the various controllable variables in an HVAC system, the settings for
supply air temperature and duct static pressure are selected specially for the study as the control
variables in the VAV air-conditioning system. Figure 1 describes a typical VAV HVAC system that
includes the control setting locations for the supply air temperature and duct static pressure.
Figure 1. Diagram of typical variable air volume (VAV) air-conditioning system and setting
controls.
3. Optimal Control Using Genetic Algorithms
In this study, GAs were used to derive optimal control parameters for an optimized control
operation. The objective function is the total energy consumption of the HVAC system. First, an
energy calculation model for the HVAC system is needed to implement a GA whose objective
function is the energy consumption of the HVAC system. The numerical model for calculating the
energy of an HVAC system references an existing calculation model and several input variables,
including controlled and uncontrolled variables [26,27]. The numerical models to determine energy
consumption of three components (fan, chiller, and cold-water pump) in the HVAC system are
addressed as follows.
Fan Power Model:
𝑄
=𝑞
1.21𝑡
𝑡
⁄, (1)
𝑄

=𝑄
𝑖

, (2)
𝑃
=𝑃
+𝐶𝑄

, (3)
𝑃

=𝑄

𝑃
𝑛
⁄, (4)
where 𝑄 is zone air flow (m
3
/h), 𝑞 is zone sensible load (kW) 𝑡 is zone temperature (°C), 𝑡 is
supply air temperature (°C), 𝑄 is total system air flow (m
3
/h), n is number of zone, 𝑃 is duct
Figure 1. Diagram of typical variable air volume (VAV) air-conditioning system and setting controls.
3. Optimal Control Using Genetic Algorithms
In this study, GAs were used to derive optimal control parameters for an optimized control
operation. The objective function is the total energy consumption of the HVAC system. First, an energy
calculation model for the HVAC system is needed to implement a GA whose objective function is the
energy consumption of the HVAC system. The numerical model for calculating the energy of an HVAC
system references an existing calculation model and several input variables, including controlled
and uncontrolled variables [26,27]. The numerical models to determine energy consumption of three
components (fan, chiller, and cold-water pump) in the HVAC system are addressed as follows.
Fan Power Model:
Qz=qs/1.21(tz−ts), (1)
Qsys =
n
X
i=1
Qzi, (2)
Pt=Ps+C×Qsys2, (3)
Pf an =Qsys ×Pt/nf, (4)
where
Qz
is zone air flow (m
3
/h),
qs
is zone sensible load (kW)
tz
is zone temperature (
◦
C),
ts
is supply
air temperature (
◦
C),
Qsys
is total system air flow (m
3
/h), n is number of zone,
Ps
is duct static pressure
(Pa), Cis flow coefficient (dimensionless), Ptis fan total pressure (Pa), nfis fan total efficiency (%).
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Chiller Power Model:
PLR =qct/qnominal ×Rf, (5)
Pchiller =Pre f ×CAPFT ×EIRFT ×EIRPLR, (6)
Pre f =qre f /COPre f , (7)
where
PLR
is part-load ratio,
qct
is system cooling coil load (kW),
qnominal
is chiller nominal capacity,
refrigeration ton (RT),
CAPFT
is a curve that represents the capacity factor as a function of evaporator
and condenser temperatures,
EIRFT
is a curve that represents the energy input ratio to cooling output
factor as a function of evaporator and condenser temperatures, EIRPLR is a curve that represents the
energy input ratio factor as a function of part-load ratio,
qre f
is chiller capacity at reference conditions
(reference temperatures and flow rates) (kW), and
COPre f
is a reference coefficient of performance
(W/W).
Pump Power Model:
Qw=qct/Cw×γw×(twr −tw), (8)
Ppump =Qw×Ht/np, (9)
where
Qw
is chilled water flow rate (L/s),
Cw
is specific heat capacity of water 4.19 (kJ/kg
◦
C),
γw
is density
of water (1000 kg/m
3
),
twr
is chilled water return temperature (
◦
C),
tw
is chilled water temperature (
◦
C),
Htis total pump head (Pa), and npis pump total efficiency (%).
The input values that are needed to calculate the energy consumption of the HVAC system are
derived from the outputs of the building simulation. The next step is to derive the optimal control
variables using GA that gives a set of optimal or potential solutions to a problem. Each solution in
the population is referred to as an individual. A generation is a new population of individuals that is
created each time [
28
]; the optimization algorithm is repeated to determine the most optimal solution.
Figure 2presents a flow chart that describes the optimization process to determine optimal control
settings using GAs.
Sustainability 2019, 11, x FOR PEER REVIEW 4 of 11
static pressure (Pa), 𝐶 is flow coefficient (dimensionless), 𝑃 is fan total pressure (Pa), 𝑛 is fan
total efficiency (%)
Chiller Power Model:
PLR = 𝑞

𝑞

⁄𝑅
, (5)
𝑃

=𝑃

 𝐶𝐴𝑃𝐹𝑇  𝐸𝐼𝑅𝐹𝑇 𝐸𝐼𝑅𝑃𝐿𝑅, (6)
𝑃

=𝑞

𝐶𝑂𝑃

⁄, (7)
where PLR is part-load ratio, 𝑞 is system cooling coil load (kW), 𝑞 is chiller nominal
capacity, refrigeration ton (RT), CAPFT is a curve that represents the capacity factor as a function of
evaporator and condenser temperatures, EIRFT is a curve that represents the energy input ratio to
cooling output factor as a function of evaporator and condenser temperatures, EIRPLR is a curve
that represents the energy input ratio factor as a function of part-load ratio, 𝑞 is chiller capacity
at reference conditions (reference temperatures and flow rates) (kW), and 𝐶𝑂𝑃 is a reference
coefficient of performance (W/W).
Pump Power Model:
𝑄
=𝑞

𝐶
𝛾
𝑡

𝑡
⁄, (8)
𝑃

=𝑄
𝐻
𝑛
⁄, (9)
where 𝑄 is chilled water flow rate (L/s), 𝐶 is specific heat capacity of water 4.19 (kJ/kg°C), 𝛾 is
density of water (1000 kg/m
3
), 𝑡 is chilled water return temperature (°C), 𝑡 is chilled water
temperature (°C), 𝐻 is total pump head (Pa), and 𝑛 is pump total efficiency (%).
The input values that are needed to calculate the energy consumption of the HVAC system are
derived from the outputs of the building simulation. The next step is to derive the optimal control
variables using GA that gives a set of optimal or potential solutions to a problem. Each solution in
the population is referred to as an individual. A generation is a new population of individuals that
is created each time [28]; the optimization algorithm is repeated to determine the most optimal
solution. Figure 2 presents a flow chart that describes the optimization process to determine
optimal control settings using GAs.
Figure 2. Flow chart of energy analysis model for HVAC system using genetic algorithms.
The GAs used for the energy calculation model and optimization of the HVAC system were
programmed using MATLAB R2018a and the toolbox in MATLAB, respectively. The hot season
Figure 2. Flow chart of energy analysis model for HVAC system using genetic algorithms.
Sustainability 2019,11, 5122 5 of 12
The GAs used for the energy calculation model and optimization of the HVAC system were
programmed using MATLAB R2018a and the toolbox in MATLAB, respectively. The hot season
(from May to September) in Seoul, Korea, was used as the analysis period for calculating the energy
consumption of the HVAC system. The total energy consumption of the HVAC system during the hot
season is the sum of the energy consumption of three components (fan, chiller, and cold-water pump)
in the HVAC system [29]. Total energy consumption can be calculated using Equation (10).
Ptotal =Pf an +Pchiller +Ppump, (10)
where Pis energy consumption. In practice, the setting values need to be selected within a controllable
range for real-world applications. The upper and lower limits were set according to the general design
conditions of the building and the literature [
30
–
32
]. The air supply temperature was selected to be
between approximately 12 and 19
◦
C, and the duct static pressure was set to be between approximately
250 and 620 Pa. Table 2shows the design settings for the two control variables (supply air temperature
and duct static pressure) and the control range for the variables.
Table 2. Control Variables at Fixed Settings and Control Range.
Case Control Variable
Supply Air Temp. (◦C) Duct Static Pressure (Pa)
‘Normal’ (Non-optimal) Control Operation 12.8 474
Optimal Control Operation (Range) Calculate GA
(12–19)
Calculate GA
(250–620)
4. Results and Discussion
4.1. Effects of Changes in Supply Air Temperature
Figure 3shows the changes in supply air temperature according to the time series during the
optimal control operation from June through September. In June and September, the supply air
temperature changes frequently, as shown in Figure 3a,d, but in July and August (the hottest months),
the supply air temperature is set as low as 12
◦
C, as shown in Figure 3b,c. If the supply air temperature is
maintained continuously at about 12
◦
C during normal operations, including July and August, the flow
rate of the cold water that circulates in the cooling coil increases, which leads to an increase in the load
rate of the refrigerator and an increase in the circulation of the cold water in the cold-water circulation
pump, thereby increasing the energy consumption of the system that supplies the cooling source.
Figure 3. Cont.
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12.8
Figure 3.
Supply air temperature changes during optimal control mode for (
a
) June, (
b
) July, (
c
) August,
and (d) September.
4.2. Effects of Changes in Duct Static Pressure
Figure 4shows the changes in duct static pressure according to the time series during the optimal
control period from June through September. Duct static pressure in the optimal control operation
mode is lower than 474 Pa and was kept constant during normal operations. The GA discovered energy
savings for the fan by supplying less air than the existing air volume by keeping the static pressure
low on the air supply side. However, any reduction in airflow that is due to low static pressure can
also cause problems such as the temporary deterioration of indoor air quality, even when the required
outdoor air intake for the design is satisfied. Hence, for practical design, ways to maintain proper
indoor air quality must also be considered.
Sustainability 2019,11, 5122 7 of 12
Sustainability 2019, 11, x FOR PEER REVIEW 7 of 11
(a) June
(b) July
(c) August
(d) September
Figure 4. Duct static pressure changes during optimal control mode for (a) June, (b) July, (c) August,
and (d) September.
4.3. Comparison of Energy Consumption Levels
Figure 4.
Duct static pressure changes during optimal control mode for (
a
) June, (
b
) July, (
c
) August,
and (d) September.
Sustainability 2019,11, 5122 8 of 12
4.3. Comparison of Energy Consumption Levels
Figure 5presents a comparison of monthly energy consumption with respect to normal operation
mode versus optimal operation mode. The total amount of energy saved in July and August when
the outside temperature is relatively high is about 4%, and is up to 9.7% in September. The rate of
the total cooling energy consumption during the period of analysis from June through September
was reduced from 384,296 kW in normal operation mode to 362,309 kW in optimal operation mode,
which represents 5.7% in energy savings. The control value changes in the time series according to the
changes in outside air conditions and the cooling load. The total energy consumption is calculated
as the sum of the separate energy consumption of the fan, chiller, and cold-water pump. The effects
of energy consumption for each of these three components in the HVAC system also were analyzed,
as discussed in the next three subsections.
Sustainability 2019, 11, x FOR PEER REVIEW 8 of 11
Figure 5 presents a comparison of monthly energy consumption with respect to normal
operation mode versus optimal operation mode. The total amount of energy saved in July and
August when the outside temperature is relatively high is about 4%, and is up to 9.7% in
September. The rate of the total cooling energy consumption during the period of analysis from
June through September was reduced from 384,296 kW in normal operation mode to 362,309 kW in
optimal operation mode, which represents 5.7% in energy savings. The control value changes in the
time series according to the changes in outside air conditions and the cooling load. The total energy
consumption is calculated as the sum of the separate energy consumption of the fan, chiller, and
cold-water pump. The effects of energy consumption for each of these three components in the
HVAC system also were analyzed, as discussed in the next three subsections.
Figure 5. Monthly energy consumption comparison between normal operation mode and optimal
operation mode for June through September.
1) Energy consumption of fan
Figure 6 shows the energy consumption and savings rates of the fan in the HVAC system
during the cooling period from June through September with respect to operation mode. The
results indicate that energy consumption is reduced by at least 15.9%–32.6% per month. The energy
savings are due to the low duct static pressure, which leads to low static pressure for the fan. The
energy consumption of the fan during the cooling months was reduced from 65,449 kW in normal
operation mode to 47,865 kW in optimal operation mode, which is a reduction of about 26.9%
energy consumption.
Figure 6. Comparison of fan energy consumption between normal operation mode and optimal
operation mode for June through September.
2) Energy consumption of chiller
Figure 7 shows the energy consumption and savings rates of the chiller in the HVAC system
during the cooling period from June through September with respect to operation mode. In June
and September, the chiller energy consumption decreased by 1.8% and 8.6%, respectively, but the
Figure 5.
Monthly energy consumption comparison between normal operation mode and optimal
operation mode for June through September.
(1) Energy consumption of fan
Figure 6shows the energy consumption and savings rates of the fan in the HVAC system during
the cooling period from June through September with respect to operation mode. The results indicate
that energy consumption is reduced by at least 15.9–32.6% per month. The energy savings are due to
the low duct static pressure, which leads to low static pressure for the fan. The energy consumption of
the fan during the cooling months was reduced from 65,449 kW in normal operation mode to 47,865 kW
in optimal operation mode, which is a reduction of about 26.9% energy consumption.
Sustainability 2019, 11, x FOR PEER REVIEW 8 of 11
Figure 5 presents a comparison of monthly energy consumption with respect to normal
operation mode versus optimal operation mode. The total amount of energy saved in July and
August when the outside temperature is relatively high is about 4%, and is up to 9.7% in
September. The rate of the total cooling energy consumption during the period of analysis from
June through September was reduced from 384,296 kW in normal operation mode to 362,309 kW in
optimal operation mode, which represents 5.7% in energy savings. The control value changes in the
time series according to the changes in outside air conditions and the cooling load. The total energy
consumption is calculated as the sum of the separate energy consumption of the fan, chiller, and
cold-water pump. The effects of energy consumption for each of these three components in the
HVAC system also were analyzed, as discussed in the next three subsections.
Figure 5. Monthly energy consumption comparison between normal operation mode and optimal
operation mode for June through September.
1) Energy consumption of fan
Figure 6 shows the energy consumption and savings rates of the fan in the HVAC system
during the cooling period from June through September with respect to operation mode. The
results indicate that energy consumption is reduced by at least 15.9%–32.6% per month. The energy
savings are due to the low duct static pressure, which leads to low static pressure for the fan. The
energy consumption of the fan during the cooling months was reduced from 65,449 kW in normal
operation mode to 47,865 kW in optimal operation mode, which is a reduction of about 26.9%
energy consumption.
Figure 6. Comparison of fan energy consumption between normal operation mode and optimal
operation mode for June through September.
2) Energy consumption of chiller
Figure 7 shows the energy consumption and savings rates of the chiller in the HVAC system
during the cooling period from June through September with respect to operation mode. In June
and September, the chiller energy consumption decreased by 1.8% and 8.6%, respectively, but the
Figure 6.
Comparison of fan energy consumption between normal operation mode and optimal
operation mode for June through September.
Sustainability 2019,11, 5122 9 of 12
(2) Energy consumption of chiller
Figure 7shows the energy consumption and savings rates of the chiller in the HVAC system
during the cooling period from June through September with respect to operation mode. In June and
September, the chiller energy consumption decreased by 1.8% and 8.6%, respectively, but the chiller
energy consumption increased by about 0.7% in July and August when the outside air temperature and
the cooling load were both high. During the cooling period from June through September, the chiller
energy consumption decreased by 1.6% from 294,820 kW in normal control mode to 290,213 kW in
optimal control mode. Thus, no significant difference was evident in the reduction rate of the energy
consumption of the chiller with regard to whether it operated in normal control mode or in optimal
control mode.
Sustainability 2019, 11, x FOR PEER REVIEW 9 of 11
chiller energy consumption increased by about 0.7% in July and August when the outside air
temperature and the cooling load were both high. During the cooling period from June through
September, the chiller energy consumption decreased by 1.6% from 294,820 kW in normal control
mode to 290,213 kW in optimal control mode. Thus, no significant difference was evident in the
reduction rate of the energy consumption of the chiller with regard to whether it operated in
normal control mode or in optimal control mode.
Figure 7. Comparison of chiller power consumption between normal operation mode and optimal
operation mode for June through September.
3) Energy consumption of cold-water pump
Figure 8 shows the energy consumption and savings rates of the cold-water pump in the
HVAC system during the cooling period from June through September with respect to operation
mode. The energy consumption of the cold-water circulation pump increased by about 1%, except
in September. When the supply air temperature is maintained at a temperature lower than about 0.8
°C compared to the temperature in normal control mode, the flow rate of the cold water that
circulates in the cooling coil increases, which results in an increase in the energy consumption of the
cold-water pump. The cooling period from June through September saw a 0.9% increase from
24,028 kW in normal control mode to 24,213 kW in optimal control mode.
Figure 8. Comparison of cold-water pump energy consumption between normal operation mode
and optimal operation mode for June through September.
5. Conclusions
The optimal control strategy proposed in this study uses an objective function as the energy
consumption in a GA. Then, GAs are used to select the supply air temperature and duct static
pressure as the control variables/settings to operate a VAV system in real time. The following
results were obtained from this study.
The proposed optimal control operation was evaluated based on changes in the optimal
control variables and energy savings in a simulated HVAC system in a reference large-scale office
Figure 7.
Comparison of chiller power consumption between normal operation mode and optimal
operation mode for June through September.
(3) Energy consumption of cold-water pump
Figure 8shows the energy consumption and savings rates of the cold-water pump in the HVAC
system during the cooling period from June through September with respect to operation mode.
The energy consumption of the cold-water circulation pump increased by about 1%, except in
September. When the supply air temperature is maintained at a temperature lower than about 0.8
◦
C
compared to the temperature in normal control mode, the flow rate of the cold water that circulates in
the cooling coil increases, which results in an increase in the energy consumption of the cold-water
pump. The cooling period from June through September saw a 0.9% increase from 24,028 kW in normal
control mode to 24,213 kW in optimal control mode.
Sustainability 2019, 11, x FOR PEER REVIEW 9 of 11
chiller energy consumption increased by about 0.7% in July and August when the outside air
temperature and the cooling load were both high. During the cooling period from June through
September, the chiller energy consumption decreased by 1.6% from 294,820 kW in normal control
mode to 290,213 kW in optimal control mode. Thus, no significant difference was evident in the
reduction rate of the energy consumption of the chiller with regard to whether it operated in
normal control mode or in optimal control mode.
Figure 7. Comparison of chiller power consumption between normal operation mode and optimal
operation mode for June through September.
3) Energy consumption of cold-water pump
Figure 8 shows the energy consumption and savings rates of the cold-water pump in the
HVAC system during the cooling period from June through September with respect to operation
mode. The energy consumption of the cold-water circulation pump increased by about 1%, except
in September. When the supply air temperature is maintained at a temperature lower than about 0.8
°C compared to the temperature in normal control mode, the flow rate of the cold water that
circulates in the cooling coil increases, which results in an increase in the energy consumption of the
cold-water pump. The cooling period from June through September saw a 0.9% increase from
24,028 kW in normal control mode to 24,213 kW in optimal control mode.
Figure 8. Comparison of cold-water pump energy consumption between normal operation mode
and optimal operation mode for June through September.
5. Conclusions
The optimal control strategy proposed in this study uses an objective function as the energy
consumption in a GA. Then, GAs are used to select the supply air temperature and duct static
pressure as the control variables/settings to operate a VAV system in real time. The following
results were obtained from this study.
The proposed optimal control operation was evaluated based on changes in the optimal
control variables and energy savings in a simulated HVAC system in a reference large-scale office
Figure 8.
Comparison of cold-water pump energy consumption between normal operation mode and
optimal operation mode for June through September.
Sustainability 2019,11, 5122 10 of 12
5. Conclusions
The optimal control strategy proposed in this study uses an objective function as the energy
consumption in a GA. Then, GAs are used to select the supply air temperature and duct static pressure
as the control variables/settings to operate a VAV system in real time. The following results were
obtained from this study.
The proposed optimal control operation was evaluated based on changes in the optimal control
variables and energy savings in a simulated HVAC system in a reference large-scale office building.
As the two optimal control operations, the air supply temperature was kept as low as 12
◦
C, which is
below the system design value of 12.8
◦
C, and the duct static pressure was kept at a value lower than
the system design value of 474 Pa.
When the low temperature and low pressure were outputs from the GAs, the total energy
consumption was reduced by 5.7%, the fan energy consumption was reduced by 26.9%, the chiller
energy consumption was reduced by 1.6%, and the cold-water pump energy consumption showed a
mere 0.9% increase. However, low supply air temperatures and low air flow rates can cause conditions
such as internal condensation in the system or ductwork, cold drafts, and temporary deterioration
of the indoor air quality during HVAC operation. Therefore, these possible outcomes need to be
addressed when optimizing HVAC system design.
If the energy consumption of the fan unit takes up a large portion of the configuration of the
HVAC system, then even more energy savings should be achieved using the proposed method.
Several variables can be controlled in supplying the heat/cooling source, such as the cold-water
supply temperature, cold-water flow rate, and cooling the water in the chiller, so further research is
required for optimizing HVAC systems using various control settings.
Author Contributions:
N.-C.S. contributed to the project idea development and write a draft version, and J.-H.K.
performed the data analysis. W.C. reviewed the final manuscript and contributed to the results discussion
and conclusions.
Funding:
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant
funded by the Ministry of Land, Infrastructure and Transport (19AUDP-B099702-05).
Conflicts of Interest:
The authors declare that there is no conflict of interests regarding the publication of
this article.
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©
2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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  • Jan 2017
  • ENERG BUILDINGS
This paper is the first part of a two-part series which investigate the energy saving potentials in a novel mechanical ventilation system by replacing terminal dampers with decentralized fans. In Part 1, a conventional variable air volume (VAV) ventilation system with dampers is studied as a reference for comparison with the novel ventilation system in Part 2. The present study (Part 1) proposes a new method to implement the pressure reset strategy in practice. A common strategy to control the supply fan speed in a VAV system is to install a pressure sensor in the supply duct system. Then the fan speed is controlled such that a constant static pressure is maintained at the sensor location. Further energy saving can be obtained by resetting the pressure setpoint to a lower value at partial load conditions. This strategy is known as pressure reset control or critical zone reset strategy in the literature. The proposed method has been evaluated through an experimental mock-up in a laboratory environment. Experimental results show a minimum reduction of about 20% in fan power demand with applying the reset strategy compared to a constant static pressure setpoint.
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Statistically Informed Static Pressure Control in Multiple-Zone VAV Systems
Article
  • Nov 2016
  • ENERG BUILDINGS
The proliferation of Building Automation Systems (BAS) has enabled the development of and use of more complex algorithms for controlling HVAC systems and increase energy efficiency in commercial buildings. One such complex algorithm, static pressure reset, which is associated with minimization of the static pressure in the supply air duct at all times while still maintaining zonal comfort —is a proven low cost means to reduce fan power consumption in Variable Air Volume (VAV) systems.
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