Simulation-based Short-term Model Predictive Control for HVAC Systems of Residential Houses - Semantic Scholar

Simulation-based Short-term Model Predictive Control for HVAC Systems of Residential Houses - Semantic Scholar
VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22

                                                     Original Article

              Simulation-based Short-term Model Predictive Control
                    for HVAC Systems of Residential Houses

                                              Nguyen Hoai Son1,∗ , Yasuo Tan2
                VNU University of Engineering and Technology, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam
              Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi City, Ishikawa, Japan

                                                Received 25 October 2018
                                  Revised 12 December 2018; Accepted 22 December 2018

          Abstract: In this paper, we propose a simple model predictive control (MPC) scheme for Heating,
          ventilation, and air conditioning (HVAC) systems in residential houses. Our control scheme utilizes a
          fitted thermal simulation model for each house to achieve precise prediction of room temperature and
          energy consumption in each prediction period. The set points for each control step of HVAC systems
          are selected to minimize the amount of energy consumption while maintaining room temperature
          within a desirable range to satisfy user comfort. Our control system is simple enough to implement in
          residential houses and is more efficient comparing with rule-based control methods.
          Keywords: Model predictive control, air conditioning, thermal simulation.

1. Introduction                                                   the operation cost of the whole system. Such kind
                                                                  of systems is called cyber physical systems (CPSs)
     With the development of computer and                         [1] and attracts a lot of attentions of researchers.
network technologies, a new paradigm of Internet                      Smart home services such as air conditioning
of Things (IoT) that things around us such as                     can bring to us a comfortable living environment,
sensors, electrical devices, ... will connect into                but also consume a large portion of electrical
a network gradually becomes a reality. In such                    energy. Nowadays, the introduction of a CPS
an environment, information of physical space                     system for smart homes, which may have
obtained by sensors can be sent into cyber space                  renewable energy sources, networked appliances
(i.e. computers), which computes the status of                    and sensors, gives us the ability to increase
the physical space and optimizes the control of                   the efficiency of energy usage in residential
actuators on the physical space in order to reduce                houses [2]. Environment data gathered by
                                                                  sensor networks, such as temperature, humidity,
    Corresponding author.
                                                                  solar radiation can be used for predicting the
    Email:                                       dynamic change of system state and optimizing
                                                                  the operation of HVAC systems. This control                method is called model predictive control (MPC).
12          N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22

MPC control strategies for HVAC systems can                  our thermal simulator used to predict the change
adapt more properly to the dynamics of thermal               of thermal environment in Section 3. In Section
environment than conventional control methods                4 and 5, we describe our MPC control scheme
such as on/off control or proportional-integral-             and performance evaluation of proposed control
derivative (PID) controls.                                   scheme. The last section concludes the paper.
     Many research on predictive model control
for HVAC systems have been done recently
[3–6]. Though MPC is a promising technology                   2. Related works
for HVAC system controls, its performance is
highly dependent on the accuracy of prediction                    The application of MPC in controls of HVAC
models. Different thermal models of a house                  systems are studied in a lot of research works
and models of HVAC systems are used to predict               [3–6, 8]. Each of them is different in prediction
the change of thermal environments and energy                model, optimization target and case study.
consumption of HVAC systems in conventional                       Many research works tries to optimize
works. However, these models are difficult to                the operation of HVAC systems based on
apply to a real house since their parameters are             time-varying electrical price for a long term
difficult to identified. Further, the cost functions         to minize the operation cost. The authors
used to optimize the operation of HVAC systems               in the paper [6] have investigated a MPC
must take into account both energy efficiency and            based supervisory controller to shift the heating
user thermal comfort.                                        and cooling load of a house to off-peak
     We have developed a thermal simulator to                hours for residential houses in Toronto Canada.
simulate the change of room temperature and the              Sturzenegger et. al. [9] reports the performance
amount of energy consumption of HVAC systems                 of MPC control strategy in a fully occupied
for real residential houses. Our simulation can              Swiss office building. In these works, since the
achieve high accuracy due to the identification              prediction horizon is long, weather forecast data is
of thermal-related parameters for each real house            used to predict the change of thermal environment.
based on experimental data [7].                              In the work [3], the uncertainty due to the use
     In this paper, we focus on MPC control                  of weather predictions is taken into account in a
strategies for HVAC systems in residential                   stochastic MPC strategy.
houses, which may include a variety of devices                    Energy consumption and thermal comfort are
such as sensors, air ventilation fans and air                both essential for the control of a HVAC system.
conditioners. We propose the utilization of                  Ascione et. el. [10] works on simulation-based
our thermal simulator to precisely predict the               MPC procedure which optimizes the hourly set
change of thermal indoor environment. Our MPC                point temperatures of HVAC system in daily
control mechanism optimizes the operation of                 operation. In the paper [11], MPC control
HVAC systems for short term durations based on               strategies are applied for a ceiling radiant heating
both energy efficiency and user thermal comfort.             system to adjust the set points of supply water
Further, it is simple enough to implement in real            temperature. In the work of J. Hu et. al. [4], MPC
house environment. Our evaluation results show               control strategies are applied for mixed-mode
that proposed MPC control mechanism can reduce               cooling including window opening position, fan
energy consumption significantly comparing with              assist, and night cooling, shading. In these papers,
a rule-based control mechanism.                              the authors try to minimize energy consumption
     The structure of the paper is follows. In               while maintaining the room temperature within a
the next section, we will describe the related               desired comfort range.
works and their limitations. We then describe                     Various kinds of thermal models are used
N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22     13

to predict the change of thermal environment                  thermal model, which calculates the change of
and energy consumption. The utilization of                    room temperature T room (t) based on the total
Building performance simulation tools – e.g.,                 amount of heat flows going out or coming in a
DOE-2 [12], EnergyPlus [13] and TRNSYS R                      room as the following equation.
[14] for prediction purpose is studied in several
                                                                           ∂T room (t)   1 X
works [10, 15]. However, since they are designed                                       =      βi Qi (t)       (1)
mainly for estimating the energy usage of a                                    ∂t        Cv i
building, they cannot be readily used for real-time
MPC control schemes. A lot of works calculate                    Here, Qi (t) is the ith heat flow going out or
room temperature based on RC modelling, which                coming in the room at time t and Cv is the heat
considers a room as a network of first-order                 capacity of the room. βi is a coefficient which
systems, where the nodes represent the room                  corresponds to the ith heat flow and is determined
temperature or the temperatures in the walls,                based on experimental data.
floor or ceiling [3, 4, 11]. Room temperature is                 In our thermal model, the heat flow Qi (t)
calculated based on heat transmission between                is calculated based on various physical models
nodes. RC modelling is easy to implement,                    which specifies thermal characteristics of a
however it is difficult to estimate parameters               room. These heat flows also depend on several
of models for real houses since the number of                environment parameters including the room
parameters is large.                                         temperatures, outside temperature, solar radiation
    Kwak et. al. [5] and En et. al. [16]                     and heat radiation of electrical devices in the room.
develop MPC control frameworks for real-time                 We calculate several kinds of heat flows going out
building energy management systems. These                    or coming in a room as follows.
implementations show the feasibility of MPC                      • Conduction heat flow through a wall or
control mechanism in real building environment.                    a window: We use a unsteady-state heat
                                                                   transfer model to calculate conduction heat
                                                                   flow through a wall. This model can take
3. Thermal simulation                                              into account the fast change of temperature
                                                                   at surfaces of walls.
    In order to simulate the change of indoor
temperature of a house, a "black-box" model,                     • Solar radiation coming in through a window:
or a "grey-box" model, or a "white-box" model                      We estimate diffuse and direct radiation from
can be used. "White-box" models, i.e. detailed                     sensor data and calculate through a window
physical models, are used in a number of thermal                   separately.
simulators such as DOE-2 [12], EnergyPlus [13]
and TRNSYS R [14]. They can evaluate thermal                     • Heat flow created by a HVAC system
load of a building from the early design phase,                  • Radiation heat from electrical devices and
however, these models require a large number of                    human bodies.
detailed thermal parameters to be specified. In the
case of modeling real houses, many parameters                     There are lots of thermal-related parameters
are uncertain and needed to be estimated by the               required for the calculation of each heat flow.
use of measurement data of external and indoor                Even though document of home plans and
thermal environment.                                          specifications of a house can be obtained, these
    In order to predict the energy consumption                parameters are not often precisely determined.
of HVAC systems and the change of thermal                     Hence, the coefficient βi in Eq. 1 corresponds
environment in a time period, we utilize a simple             to the heat flow Qi (t), which can be considered
                                                              as a representative parameter for all uncertain
14                N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22

                                                                                                                                    MPC controller
Communication              Sensor data          Operation schedule                                                                         Thermal simulation
   module                  acquisition             acquisition
                                                                                                    HVAC      commands
                                            Room             Air                         interact   systems                                   Heat flux
                       Heat flux         temperature      conditioner        Thermal                                        Command
      Thermal         calculation                                          environment                                      Optimizer        calculation
     simulation                           calculation     simulation                                          sensor data
                                                                                                    Sensors                                     Room
       module                                                                            sensing
                             Thermal parameter estimation

      Home             Room               Thermal
                                                         Training data
     modeling         modeling             feature
                                         extraction                           Figure 2. System model of MPC controller for
                                                                                             HVAC systems.
                       House                                                 gets weather forecast data from an online
                                                           data files        weather station. It then sends the data to the
                                                                             thermal simulation module to perform online
     Figure 1. Structure of proposed thermal simulator.                      prediction of energy consumption.
parameters involved in the calculation of the
heat flow Qi (t). We only need to estimate these
coefficients, whose number is small, by the use                          4. Model predictive control for HVAC systems
of training data. Therefore, our thermal simulator
can achieve high accuracy comparing with actual                          4.1. System model
measurement data.
                                                                             System model for MPC controller of HVAC
     Our simulator is constructed from three
                                                                         systems is shown in Fig. 2. The system includes
following modules (Fig. 1).
                                                                         following elements.
  • Home modeling module: models a house as                                • Sensors installed in a house which collect
    a number of rectangular rooms adjacent to                                environment data and send to a MPC
    each other and each room contains a number                               controller. The sensor data may include
    of walls and windows. The module reads                                   the data of outside temperature, outside
    parameters related to thermal characteristics                            humidity, solar radiation, ...
    of walls and windows from a number of
    configuration files and creates an object to                           • HVAC system which interacts with the
    store the structure information of the house.                            thermal environment of the house. A HVAC
    It also reads offline environment data such as                           system may include air conditioners, heaters,
    temperature, humidity, wind velocity, data of                            ventilation fans, curtain controllers, ...
    solar radiation as training data from sensor                           • MPC controller which receives data from
    data files.                                                              sensors and selects optimized control set for
  • Thermal simulation module: The module                                    the HVAC system based on the prediction
    calculates the change of room temperature                                results of a thermal simulator
    by calculating conduction heat, solar                                  • Thermal simulator which gets input data
    radiation, radiation heat from electrical                                from MPC controller and predicts the change
    devices and heat removed or generated by                                 of room temperature and the amount of
    air conditioners. We uses training data                                  energy consumption of the HVAC system.
    to identify unknown thermal parameters of                                The input data includes sensor data and
    thermal models.                                                          command sets for the HVAC system
  • Communication module: This module gets                                  We consider a HVAC system for residential
    data from sensor installed in a house and                            houses which includes air conditioners, which
N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22     15

consume electrical energy to produce heating                  the specific heat of the air (J/kg.K), T air (t) and
energy or remove thermal energy from a room                   T room (t) are the temperature of outside air and
and ventilation systems, which bring in fresh air             the room at the time t. The air flow rate can
in the house. The HVAC system has constraint                  be controlled between different levels. Electrical
on the timing that the room environment should                energy consumed by a ventilation system (J/s)
reach the target temperature.                                 is calculated based on the air flow rate as the
     There are two types of air conditioners,                 following equation.
non-inverter air conditioners and inverter air
conditioners. Since the inverter air conditioners                                Ev (t) = αv Va (t)           (5)
become popular due to their energy efficiency, we
utilize a simple model of PID control to simulate             where αv is a coefficient of the ventilation system.
the control of inverter air conditioners [? ]. The                 In this paper, we consider a control scenario
amount Qa (t) of heat flow in a time unit (J/s)               in which a user is on the way back home and
created by an air conditioner using PID control is            he will arrive home after a time period. His
calculated based on the following equation.                   scheduler sends a command to the HVAC system
                                                              of his house. The HVAC system must regulate
                        Z t
                                        de(t)                 room temperature to reach a desired range of
  Qa (t) = KP e(t) + KI     e(τ)dτ + KD         (2)           temperature when the use comes back home.
                         0                dt
                                                                   It is difficult to precisely predict the change
    Here, e(t) is the difference between                      of room temperature and the energy consumption
room temperature (T room ) and setting                        of a HVAC system for a long time period since
temperature(T setting ). The parameters KP , KI ,             they depends on various environment parameters
and KD are the coefficients for the proportional,             such as outside temperature, outside humidity,
integral, and derivative terms of PID control and             solar radiation which are difficult to predict for
are estimated based on training data. Electrical              a long time period. However, these environment
energy consumed by air conditioner are calculated             parameters do not change much for a short-time
by the following equation.                                    period. Therefore, we propose a MPC control
                            1                                 mechanism with short time prediction horizon
                Ea (t) =       Qa (t)              (3)        to ensure the accuracy of prediction results.
                                                              Furthermore, our MPC control mechanism only
where COP is the coefficient of performance of                manipulates the setting points of a HVAC system
the air conditioner.                                          such as the setting temperature of air conditioners
    Since the amount of ventilation heat flowing              and the air flow rate of ventilation fans. Hence, it
into a room depends on the amount of ventilation              is easy to implement in residential houses.
air, the specific heat of the air and the
difference between outside temperature and                    4.2. MPC control strategy
indoor temperature, we consider that the amount
of ventilation heat flow created by a ventilation                 The purpose of our control algorithm is
fan can be modeled by the following equation.                 to minimize the amount of electrical energy
                                                              consumption of the system while maintaining the
        Qv (t) = ρVaCa (T air (t) − T room (t))    (4)        room temperature within a desired temperature
                                                              range during a desired time period.        For
where Qv (t) is the amount of heat entering the               example in a summer day, outside temperature
room due to air ventilation per a time unit(J/s),             may be lower than room temperature. When
ρ is the density of the air (kg/m3 ), Va is the air           the difference is large, natural or mechanical
flow rate of the ventilation system (m3 /s), Ca is            ventilation should be used since it can reduce
16          N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22

the room temperature with little electrical energy.
However, when the difference is small, air                                                      S2    ...
conditioner should be used since the energy                                     S1
efficiency of air ventilation becomes small.                     S0

     Our idea of applying MPC control strategies                   t0          t1              t2     ...     tk-1   tk
for the optimization of HVAC system operation is                                    a) One-step prediction
shown in Fig. 3. When a MPC controller receives
a request message containing the desired time
period from its user, it divides the operation period                                           S2    ...
starting from the request receiving time until the                              S1
end of the desired time period into a number of                  S0
control steps. In one control step, a command set                  t0          t1              t2     ...     tk-1   tk
(i.e. the set points of the HVAC system) is kept
                                                                                    b) Multiple-step prediction
     We need to find out the command set, which                Figure 3. Selections of command sets for HVAC system.
can optimize the operation of the HVAC system.               large number of control steps, the computation
In order to do that, the MPC controller of a                 cost will be high since the cost of performance
house receives sensor data at the beginning of               evaluation of a HVAC system for one command
a control step and sends the data to the thermal             set is high and the number of all possibilities
simulator to update the present environment status           of command sets that need to be evaluated is
of the house. It then calculates all possibilities of        large. Further, long prediction horizon will
command sets within the prediction period started            make the error of prediction results increase
from the beginning of the control step and send              since environmental variables such as outside
each of setting points to the thermal simulator.             temperature, outside humidity, solar radiation may
The thermal simulator will calculate the change              change heavily within a long prediction period
of room temperature and energy consumption                   while their data remain unchanged during the
based on each command set. Here, environmental               simulation. Hence, the prediction horizon need to
parameters, e.g. outside temperature, solar                  be selected carefully.
radiation, are supposed to be unchanged during
the prediction period.                                        4.3. Cost functions
     The MPC controller selects the command
set which gives the best performance and send                      Whenever the MPC controller sends a
the control commands corresponding to the                     command set to the thermal simulation, the
present control step to the HVAC system. After                thermal simulation will return the prediction
the HVAC system actually interacts with the                   results of the change of room temperature and
thermal environment under the sent control                    the amount of energy consumption within the
commands, the MPC controller repeats the                      prediction period. In order to select the best
previous operations for the next control step.                command set for HVAC system in each prediction
     The prediction horizon for MPC control                   period, we need a cost function which can evaluate
scheme may be one control step (Fig. 3 a)                     the efficiency of HVAC system and thermal
or several control steps (Fig. 3 b). If the                   comfort based on prediction results.
prediction horizon is only one control step, the                   Thermal comfort can be evaluated based on
MPC controller can only optimize the system                   several parameters such as temperature, humidity,
efficiency in that control step but not the whole             air velocity but the main factor is temperature [18].
operation period. If the prediction horizon is a              In this research, for simplicity, we only use room
                                                              temperature to evaluate thermal comfort. If the
N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22                17

room temperature is outside the range of desired                          the room temperature to reach to the desired
temperature, the user will feel uncomfortable.                            temperature range. However, Qtarget affects
Therefore, we define a thermal discomfort index                           the temperature control target only when the
for a time period [t s , te ] as follows.                                 operation time of HVAC system is not long
                                Z te                                      enough to manipulate the room temperature.
             Udiscom f ort =         U(t)dt  (6)                          Therefore, if the ending time of the prediction
                                         ts                               period is not within the desired time period, we
                                                                          use the following cost function to select the best
                                                                         command set for HVAC system.
         0                                         min
                                 if T room (t) ∈ [T target , T target
  U(t) = 
          T      (t) −  T max
                                 if T      (t) > T max                                                      1
           room          target      room         target                              Fcost = S +                           (7)
                   − T room (t) if T room (t) < T target
                                                   min                                               N step Qtarget
         T target

     Here, T room (t) is the room temperature at                          where N step is the number of control steps from
the time t and [T targetmin , T max ] is the range of
                                target                                    the end time of the prediction period to the
predetermined user desired temperature.                                   beginning time of the desired time period.
     In order to change the temperature of a room                             If the ending time of a prediction period
to the desired temperature range, a HVAC system                           is within the desired time period, we will
consumes electrical energy to remove heat from                            select a command set, which minimizes thermal
the room in the case of cooling or add heat to the                        discomfort index. If there are multiple command
room in the case of heating. Thus, the efficiency                         sets that minimize thermal discomfort index, we
of the HVAC is evaluated based on the amount                              will select the command set which consumes the
of energy consumption E HV AC and the amount of                           minimum amount of energy consumption. Hence,
heat removed from or added to the room, which is                          the following cost function is used.
calculated based on the room temperature at the
beginning and at the end of the prediction period                               Fcost = (1 + Udiscom f ort )(Ω + E HV AC )   (8)
(T s and T e ), as follows.
                                                                             Here, E HV AC is the electrical energy
                start − T end ) − E
       Cv (T room
                                    HV AC if cooling                     consumed by the HVAC system in a prediction
  S =                   room
                end − T start ) − E
       Cv (T room                                                        period, Udiscom f ort is the thermal discomfort index
                                     HV AC if heating
                                                                          for the prediction period, calculated by Eq. 6.
     We consider that if the ending time of a                             Ω is a constant number, which is big enough
prediction period is not within the desired time                          to leverage the thermal discomfort index when
range, we should maximize the efficiency of the                           E HV AC is 0.
HVAC system. However, if the ending time of the
prediction period is close to the beginning of the
desired time range, we also need to consider the                          5. Evaluation
amount of heat Qtarget that needs to be removed
from the room in order for the room temperature                           5.1. Evaluation environment
to reach to the desired temperature range.
                                                                              In order to verify the efficiency of our
                     end − T max ) if cooling
              Cv (T room
                                                                         proposed method, we implement our control
    Qtarget =               target
                     min − T end ) if heating
              Cv (T target
                                                                         system in MATLAB/Simulink, which is a very
                                                                          powerful program to perform numerical and
    The larger the heat amount Qtarget is, the                            symbolic calculations, and is widely used in
longer time the HVAC system must take to get                              science and engineering.
                                                                              Since the weather conditions are different
18                     N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22

                                                                    MPC controller                                       34
             Thermal simulation
                     HVAC                                                      HVAC                                      32
                   simulation             HVAC                               simulation

                                                                                                      Temperature( oC)
 Sensor                                                                                                                  30
  data              Heat flux                                                 Heat flux
                                                       Set point
  files            calculation           Sensor                              calculation
                                         data                                                                            28
                      Room                                                      Room
                   temperature                                               temperature
                    calculation                                               calculation                                26
                                                                                                                                                         Room temperature (experiment)
                                                                                                                         24                              Room temperature (simulation)
                                                                                                                                                         Outside temperature
                    Figure 4. MPC control simulation.                                                                    22
                                                                                                                              0   3    6     9      12       15         18        21     24
                                              N                                                                                                  Time (h)

        n                                              Spare room             Bedroom B                                  Figure 6. Room temperature and outdoor temperature
                                                                                                                                      during the experiment day.

                                                                                            7.280 m   Table 1. List of control commands for experimental HVAC
     Living room         Japanese room                   Master bedroom      Bedroom A
                                                                                                        Ventilation fan
              8.645 m                                                                                   Control command Operation
              First floor                                           Second floor                                 0             turn OFF
                                                                                                                 1             turn ON and L speed = 1
                        Figure 5. Structure of iHouse.                                                           2             turn ON and L speed = 2
                                                                                                        Air conditioner
when performing each control scheme, we do not                                                          Control command Operation
perform experiments but instead use simulation                                                                   0             turn OFF
to evaluate the effectiveness of proposed control                                                                1             turn ON and T setting = T target
algorithm. As shown in Fig. 4, in each control                                                                   2             turn ON and T setting = T target − 1
                                                                                                                 3             turn ON and T setting = T target − 2
step the thermal simulator reads sensor data at the
the beginning time of the control step from file
storage and sends sensor data to MPC controller,                                                      have one or more windows. The object of our
which calculates set points of HVAC systems                                                           verification is Bedroom A of the iHouse (Fig. 5).
for the control step and sends back the inputs                                                            The experiment day is 14th August 2012.
to the thermal simulator. The thermal simulator                                                       The outside temperature and the temperature of
then reads sensor data for the whole control step                                                     Bedroom A of the iHouse without any operation
and perform simulation to calculate the change                                                        of HVAC system during this day are shown in
of room temperature and energy consumption of                                                         Fig. 6. The outside temperature is lower than
HVAC system in the control step. It then sends                                                        the room temperature in all day. We perform
sensor data at the end of the control step as the                                                     thermal simulation for this day to confirm the
sensor data at the beginning of the next control                                                      accuracy of simulation results. As shown in Fig.
step and the simulation is repeated until the end                                                     6, the mean deviation of simulation results is
of simulation.                                                                                        0.23 degree centigrade. It means that our thermal
    We perform our simulation targeted on a                                                           simulator can achieve high accuracy.
real house called iHouse, which is a testbed for                                                          In order to verify the efficiency of our
smart home services. The iHouse is located                                                            proposed method, we perform three following
at Ishikawa prefecture, Japan. It is a typical                                                        control scenarios.
2-floor Japanese-style house, which can divide
into 15 rooms. Appliances in iHouse such                                                                                 • Rule-based control mechanism: When
as air conditioners, wattmeters and sensors are                                                                            receives the request, turns on the ventilation
connected to the network via ECHONET lite                                                                                  fan when the temperature of outside air is
protocol [17]. Most of the rooms in the house                                                                              higher than room temperature. Turn off the
N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22                                                                             19

                    34                                                                       400                                                 34                                                             300

                                                                                                                                                                                                                      Consumption energy (Wh)
                                                                                                                                                                                  Outside temperature

                                                                                                   Consumption energy (Wh)
                    33                                  Outside temperature                                                                      33
                                                                                                                                                                                  Room temperature              250
                                                        Room temperature
                    32                                                                       300                                                 32                               Consumption energy
Temperature (oC)

                                                        Consumption energy

                                                                                                                             Temperature ( oC)
                    31                                                                                                                           31
                    30                                                                       200                                                 30                                                             150
                    29                                                                                                                           29
                    28                                                                       100                                                 28
                    27                                                                                                                           27
                    26                                                                        0                                                  26                                                              0
                      17                   17.5            18                 18.5          19                                                     17              17.5            18                   18.5   19
                                                         Time (h)                                                                                                                Time (h)

                                                  Fan command       AC command                                                                                            Fan command       AC command

                                                                                                                                     Control command
                            2                                                                                                                          2
          Control command


                             17            17.5            18                 18.5          19                                                         17          17.5            18                   18.5   19
                                                         Time(h)                                                                                                                 Time(h)

                                Figure 7. Results of rule-based control algorithm                                                                           Figure 8. Results of MPC control algorithm
                                       (temperature range: 26-27 degree).                                                                                       (temperature range: 26-27 degree).
                                Table 2. Simulation parameters of HVAC system                                                the desirable range right after his arrival time (i.e.
                                  Parameter                                   Value                                          the target time). In our simulation, the target time
                                  Control step                                10 minutes                                     is 18:00 while the notification time of user arriving
                                  Room heat capacity                          11224 J/K
                                  Air conditioner                                                                            is 1 hour before the target time (i.e. 17:00). The
                                  - COP                                       3.05                                           simulation lasts until 19:00.
                                  Ventilation fan                                                                                 In order to find out the best solution
                                  - Air flow         L speed        =1        0.097 m3 /s                                    of command control set of HVAC system
                                                     L speed        =2        0.146 m3 /s
                                  - Electrical power L speed        =1        31 W
                                                                                                                             in a prediction time period, we utilize a
                                                     L speed        =2        53 W                                           simple brute-force algorithm which searches all
                                                                                                                             possibilities of control sets in a prediction time
                                                                                                                             period. The number of control sets is proportional
                                ventilation fan and turn on the air conditioner
                                                                                                                             to the exponential of the number of control steps .
                                30 minutes before the target time
                      • Proposed MPC control mechanism                                                                       5.2. Simulation results

    We simulate a HVAC system, which includes                                                                                    In the simulation of proposed MPC control
an inverted air conditioner and a ventilation fan in                                                                         scheme, we set the time duration of one control
the room. We can set the setting temperature for                                                                             step to be 10 minutes. The prediction time is 20
the air conditioner and turn on/off the fan. Hence,                                                                          minutes, twice as the time duration of a control
the input command includes two parameters for                                                                                step. We simulate two cases:
ventilation fan and air conditioner. The operation
corresponding to each control command is listed                                                                                                        • The range of user desired temperature is set
in Table 1. Simulation parameters are described                                                                                                          to [26◦ C-27◦ C].
in Table 2.                                                                                                                                            • The range of user desired temperature is set
    We consider an application scenario when a                                                                                                           to [25◦ C-26◦ C].
user is going to come back home. The user may
send a notification message including his arrival                                                                                When the desired temperature range is set
time to the HVAC system. The HVAC system                                                                                     to [26◦ C-27◦ C], in rule-based method, the air
must control the room temperature to be within                                                                               conditioner is turned on at highest speed level
20                                    N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22

                     34                                                             400                                                       34                                                                   400

                                                                                          Consumption energy (Wh)
                                                      Outside temperature                                                                                                           Outside temperature

                                                                                                                                                                                                                            Consumption energy (Wh)
                     32                               Room temperature                                                                        32                                    Room temperature
Temperature ( o C)

                                                                                                                        Temperature ( C)
                                                      Consumption energy            300                                                                                             Consumption energy

                     30                                                                                                                       30
                                                                                    200                                                                                                                            200
                     28                                                                                                                       28
                                                                                    100                                                       26

                     24                                                              0                                                        24                                                                    0
                       17                 17.5            18                18.5   19                                                           17                 17.5            18               18.5          19
                                                       Time (h)                                                                                                                 Time (h)
                                                 Fan command      AC command                                                                                              Fan command       AC command
         Control command


                                                                                                                                  Control command
                           0                                                                                                                        0
                            17            17.5            18                18.5   19                                                                17            17.5            18               18.5          19
                                                       Time(h)                                                                                                                  Time(h)

                                 Figure 9. Results of rule-based control scheme                                                                             Figure 10. Results of MPC control scheme
                                      (temperature range: 25-26 degree).                                                                                       (temperature range: 25-25 degree).
30 minutes before the target time and the setting                                                                                             400

temperature is set to the target temperature (i.e. 27                                                                                         380                                   Target temperature = 27 o C
                                                                                                                    Energy consumption (Wh)

                                                                                                                                                                                    Target temperature =26 o C
degree centigrade). The simulation results (Fig. 7)
show that the room temperature reach the desired
temperature range at the target time while the
amount of energy consumption is 318.53 Wh.                                                                                                    320

     Proposed MPC control scheme (Fig. 8) turns                                                                                               300

on the ventilation fan at level 1 from 17:30 to                                                                                               280
17:50. It then turns on the air conditioner with                                                                                              260
setting temperature of 26 degree centigrade from                                                                                                        1             2                 3
                                                                                                                                                                            Prediction horizon (steps)
                                                                                                                                                                                                           4            5

17:50 to 18:10. It then sets the setting temperature
of the air conditioner to be 27 degree centigrade.                                                                  Figure 11. Results of energy consumption with the change
As the result, the room temperature reach the                                                                                       of the prediction horizon.
desired temperature range at the target time while                                                                  air conditioner then changes to be 25 degree
the amount of energy consumption is 272.47 Wh,                                                                      centigrade from 18:00 to 18:20 and changes to
14.4% lower than the energy consumption using                                                                       be 26 degree centigrade from 18:20 to 19:00.
rule-based control scheme.                                                                                          As the result, the room temperature reach the
     When the desired temperature range is set                                                                      desired temperature range at the target time while
to [25◦ C-26◦ C], in rule-based method, the room                                                                    the amount of energy consumption is 318.7 Wh,
temperature cannot reach the desired temperature                                                                    13.4% lower than the energy consumption using
range at the target time (i.e. 18h00) and it only                                                                   rule-based control.
reaches the desired temperature range at 18h20                                                                          The evaluation results show that proposed
(Fig. 9). The amount of energy consumption is                                                                       MPC control scheme is more flexible and can
368.1 Wh.                                                                                                           achieve better energy efficiency with better user
     Proposed MPC control scheme (Fig. 10) turns                                                                    comfort comparing with rule-based control.
on the ventilation fan at level 1 from 17:00 to                                                                         We change the prediction horizon which is
17:50. It then turns on the air conditioner with                                                                    multiple times of control step duration. As shown
setting temperature of 24 degree centigrade from                                                                    in Fig. 11, when the prediction horizon is only
17:50 to 18:00. The setting temperature of the                                                                      one control step, the energy consumption of
N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22              21

HVAC system is 14.85% more than when the                      and National Institute of Information and
prediction horizon is only two control steps. It              Communications Technology (NICT)
is because the MPC controller only optimizes
the system efficiency in one control step but not
the whole operation period.However, the energy                References
consumption of HVAC system only decreases
                                                               [1] R. Rajkumar, I.L.I. Lee, L.S.L. Sha, J. Stankovic,
little with the increase of the prediction time                    Cyber-physical systems:         The next computing
duration. Since the inaccuracy of prediction                       revolution, in: Proceedings of the 47th Design
results may increases when the prediction horizon                  Automation Conference. (2010) 731–736.
is long, the performance of the system even              
                                                               [2] M. Schmidt, C. Åhlund, Smart buildings as
becomes worse in several cases. Further, when the
                                                                   Cyber-Physical Systems: Data-driven predictive
prediction time duration is longer, the calculation                control strategies for energy efficiency, Renewable and
cost for best solution of control command set                      Sustainable Energy Reviews. 90 (2018) 742–756.
is high since the size of searching space is             
                                                               [3] F. Oldewurtel, A. Parisio, C.N. Jones, D. Gyalistras,
proportional to the exponential of the control steps
                                                                   M. Gwerder, V. Stauch, B. Lehmann, M. Morari, Use
in one prediction time duration.                                   of model predictive control and weather forecasts for
                                                                   energy efficient building climate control, Energy and
                                                                   Buildings. 45 (2012) 15–27.
6. Conclusion                                            
                                                               [4] J. Hu, P. Karava, Model predictive control strategies
                                                                   for buildings with mixed-mode cooling, Building and
    In this paper, we propose the utilization                      Environment. 71 (2014) 233–244.
of our fitted thermal simulation to predict the          
change of room temperature and the amount                      [5] Y. Kwak, J.H. Huh, C. Jang, Development of a
                                                                   model predictive control framework through real-time
of energy consumption of HVAC system. Our
                                                                   building energy management system data, Applied
proposed MPC control scheme optimizes the                          Energy. 155 (2015) 1–13.
control of HVAC system in a short prediction             
horizon based on a cost function, which can take               [6] A. Afram, F. Janabi-Sharifi, Supervisory model
into account both energy consumption and user                      predictive controller (MPC) for residential HVAC
                                                                   systems: Implementation and experimentation on
thermal comfort. The evaluation results show                       archetype sustainable house in Toronto, Energy and
that our system can achieve good performance                       Buildings. 154 (2017) 268–282.
comparing with rule-based control scheme.                
    In the future works, we will work on the                   [7] H. Nguyen, Y. Makino, A.O. Lim, Y. Tan, Y.
                                                                   Shinoda, Building high-accuracy thermal simulation
improvement of calculation delay to realize                        for evaluation of thermal comfort in real houses, in:
realtime energy management for residential                         Lecture Notes in Computer Science, Vol. 7910 LNCS,
houses. We also apply MPC control mechanism                        Springer, Berlin, Heidelberg. 2013, pp. 159–166.
for other devices such as heaters or curtains.           
                                                               [8] R. De Coninck, L. Helsen, Practical implementation
                                                                   and evaluation of model predictive control for an office
Acknowledgments                                                    building in Brussels, Energy and Buildings. 111 (2016)
    This work has been partly supported by
                                                               [9] D. Sturzenegger, D. Gyalistras, M. Morari, R.S. Smith,
Vietnam National University, Hanoi (VNU),                          Model Predictive Climate Control of a Swiss Office
under Project No. QG.16.30.                                        Building: Implementation, Results, and Cost-Benefit
    This work is also partly supported by the                      Analysis, IEEE Transactions on Control Systems
joint research project between Japan Advanced                      Technology. 24(1) (2016) 1–12.
Institute of Science and Technology (JAIST)                   [10] F. Ascione, N. Bianco, C. De Stasio, G.M. Mauro,
22             N.H. Son, Y. Tan / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 1 (2019) 11–22

       G.P. Vanoli, Simulation-based model predictive control   [15] M. Wallace, R. McBride, S. Aumi, P. Mhaskar, J.
       by the multi-objective optimization of building               House, T. Salsbury, Energy efficient model predictive
       energy performance and thermal comfort, Energy and            building temperature control, Chemical Engineering
       Buildings. 111 (2016) 131–144.                                Science. 69(1) (2012) 45–58.      
[11]   J. Široký, F. Oldewurtel, J. Cigler, S. Prívara,         [16] O. Sian En, M. Yoshiki, Y. Lim, Y. Tan, Predictive
       Experimental analysis of model predictive control for         thermal comfort control for cyber-physical home
       an energy efficient building heating system, Applied          systems, in: Proceedings of 2018 13th Annual
       Energy. 88(9) (2011) 3079–3087.                               Conference on System of Systems Engineering (SoSE).               (2018) 444–451.
[12]   James J. Hirsch, DOE-2 Building Energy              
       Use      and     Cost     Analysis    Tool     (2013).   [17] A.I. Dounis, C.Caraiscos, Advanced control systems (accessed 24                  engineering for energy and comfort management
       October 2018)                                                 in a building environment—a review, Renewable
[13]   US Department of Energy, Energy Efficiency                    and Sustainable Energy Reviews. 13(6-7) (2009)
       and Renewable Energy Office,                 Building         1246–1261.
       Technology Program, EnergyPlus 8.9.0 (2018).         (accessed 24 October 2018)        [18] P. Höppe, The physiological equivalent temperature -
[14]   Solar Energy Laboratory, TRNSYS 18: A Transient               A universal index for the biometeorological assessment
       System Simulation Program, University of Wisconsin,           of the thermal environment, International Journal of
       Madison. (accessed 24           Biometeorology. 43(2) (1999) 71–75.
       October 2018)                                       
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