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arXiv:1606.08410v1 [] 27 Jun 2016

Building Airflow Monitoring and Control using
Wireless Sensor Networks for Smart Grid
Application
Nacer Khalil

Driss Benhaddou

Abdelhak Bensaoula

Michael Burriello

Computer Science Department
University of Houston

Engineering Technology Department
University of Houston

Physics department
University of Houston

Facilities Management
University of Houston

Raymond E Cline, Jr.
Information and logistics Technology Department
University of Houston

Many of the rooms in every building are cooled without being
used which represents a loss. Some of the buildings are rarely
used while others are used very differently throughout the day
(e.g. labs are many times empty and other times full). This
results in considerable inefficiency when maintaining different
rooms equally.

Abstract—The electricity grid is crucial to our lives. Households and institutions count on it. In recent years, the sources of
energy have become less and less available and they are driving
the price of electricity higher and higher. It has been estimated
that 40% of power is spent in residential and institutional
buildings. Most of this power is absorbed by space cooling and
heating. In modern buildings, the HVAC (heating, ventilation,
and air conditioning) system is centralised and operated by
a department usually called the central plant. The central
plant produces chilled water and steam that is then consumed
by the building AHUs (Air Handling Units) to maintain the
buildings at a comfortable temperature. However, the heating
and cooling model does not take into account human occupancy.
The AHU within the building distributes air according to the
design parameters of the building ignoring the occupancy. As
a matter of fact, there is a potential for optimization lowering
consumption to utilize energy efficiently and also to be able to
adapt to the changing cost of energy in a micro-grid environment.
This system makes it possible to reduce the consumption when
needed minimizing impact on the consumer. In this study, we
will show, through a set of studies conducted at the University
of Houston, that there is a potential for energy conservation and
efficiency in both the buildings and the central plant. We also
present a strategy that can be undertaken to meet this goal. This
strategy, airflow monitoring and control, is tested in a software
simulation and the results are presented. This system enables the
user to control and monitor the temperature in the individual
rooms according the locals needs.
Index Terms—Airflow management, WSN, Zigbee, Energy
efficiency, CPS, micro-grid

The way the current air-handling units work is that it ties
all rooms with constant quality of cooled air. This keeps them
at the same temperature, but some of the air is removed even
if it is cool enough to be kept. In addition, there is no way to
customize the temperature according to one’s needs. Usually,
one cannot change the rooms’ temperature without affecting
the temperature of the other rooms on the same floor. This is
a major drawback of the current air handling unit in addition
to loss it creates when not accounting of the occupancy of the
rooms.
The drawbacks of the current system are an important
motivation for our team to build an overlying system that
solves these drawbacks by increasing users’ comfort and
decreasing the energy loss. This system uses a Wireless Sensor
Network (WSN) to sense the temperature and to control the
quantity of air flowing into every room.
The main challenge was to design the air flow management
system without replacing the current system. We propose a
system that uses WSN where each sensor node is placed
in room of the building and another one placed in the air
distribution system. This makes it possible to sense in realtime the temperature and then, choosing the appropriate angle
to open the damper through which air will flow into the room
based on a model. The model will be presented, in addition
to a simulation that was conducted to test the system’s ability
to serve the user and increase energy efficiency.

I. I NTRODUCTION
Energy scarcity is increasingly becoming an important challenge that nations are faced to solve. In fact, 40% of the energy
is spent in buildings [1], and a significant amount is used for
space cooling/heating. This is the case for the University of
Houston, located in Houston, Texas, known for being a very
warm and humid city. As a result, it spends a considerable
amount of money to maintain a convenient temperature and
humidity level. It counts on modern and highly efficient HVAC
(heating, ventilation, and air conditioning) systems to maintain
the buildings at a convenient temperature and humidity level.

The rest of the paper is organized as follows: Section
II presents the background and related. In section III, the
methodology is presented. In section IV, the results of the
simulation are presented and discussed. Section V concludes
the paper and presents the future work.
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II. BACKGROUND AND RELATED WORK

compression cycle (VCC) in an air-conditioning system, containing refrigerant which is used to provide cooling. Another
paper on Experimental investigation of wraparound loop heat
pipe heat exchanger used in energy efficient air handling units
[13]. This is an experimental investigation on the thermal
performance of an air-to-air heat exchanger, which utilises heat
pipe technology. While most of the research is in the area of
HVAC (Heating, Ventilation and Air Conditioning) our model
deals with only air flow.

Energy is one of the most important concerns of the world.
As fossil fuels become less and less available, their price has
increased significantly in the last decade, and raises concerns
for future generations’ energy outlook. Electricity is mostly
generated using coal, which has a huge negative impact on
the environment [2].
In recent years, due to high electricity cost, high growth
rate of the electric grid and climate change, the one centuryold electric grid has seen a drastic shift in its internal design
and philosophy [3]. The smart grid is intended to support
Distributed Energy Generation (DER), two-way power and
communication flow between the producer and consumers of
power, and increased usage of renewable energies [4]. This
impacts the price of electricity where it becomes variable
and changes very frequently [5]. The user can benefit by
minimizing the usage when the price is high and shift the
necessary tasks to the times when the price is low. However,
the user cannot do this manually as it is very time consuming.
Smart buildings have the potential to automate the process in
addition to minimizing the losses in a building [6].
In an industrial environment, the buildings count on the
services delivered by the central plant for providing electric
power with high reliability for cooling and heating. The central
plant is an efficient way to manage power, because it uses the
economy of scale of larger machinery that is more efficient to
serve multiple buildings rather than smaller machinery that is
neither as efficient nor as reliable [7].
The buildings are the main consumers of electric power,
chilled water and steam. They use these three sources to power
the users’ machinery and provide them with a comfortable
space for living [8]. The buildings can be optimized to avoid
energy loss[9].
The buildings as well as the central plant can become more
efficient using more automation that takes into consideration
the electricity price, human activity and machines efficiency
profiles. In [10], they use sensing to monitor the building’s
consumption as well as the users’ occupancy and incorporate
this data into agents to estimate the occupants in the building,
predict them and adjust the building’s consumption to this
changing environment. In [11], sensory data is collected every
fifteen minutes in order to determine the consumption of the
building in the next hour.
In modern buildings, there are Air Handling Units that
consume chilled water and steam to adjust the environment
for the occupant. There are different types of Air Handling
Units, such as fan Coils, which are small and simple and
are commonly used in smaller buildings and commercial
applications. On the other hand, there are custom AHUs
available in any configuration that a user might require, which
are very common for institutional and industrial applications.
There are a number of research activities towards saving the
energy consumption in Buildings. One of those is an energy
efficient model predicting building temperature control [12].
This work focuses on the problem of controlling the vapor

III. DATA GATHERING AND ANALYSIS
In this section, we show the different studies that were
undertaken to investigate the potential for energy optimization
in the central plant and the buildings. The gathered data for
the central plant is a collection of data logs that monitor the
status of every machine of the central plant every two hours.
We used four months of data for this study.
A. Central plant optimization
The central plant is composed of a set of machinery that
provide chilled water and steam. These two systems are
independent as they do not share any machinery. However,
each of these systems is composed of a set of machines
that work hand in hand to provide the required service.
To develop the Airflow Management System using Sensor
Networks, different parameters had to be taken into account
to design the system which enables a user to control the
air temperature of the room. To understand the scenario, let
us consider the schematic in Figure 1. The main types of
machines that are used are: chillers, chilled water pumps,
condenser water pumps and cooling towers. For each type,
there are a number of machines consisting of different brands,
different ages and different usage hours. As a result, the
efficiency profile of every machine is different and thus affects
uniquely the efficiency of the whole system.

Fig. 1.

University of Houston Central plant machines map

To evaluate the efficiency of the chiller system, we used the
following measure called KFG, which stands for the number
of KiloWatt Hour required to cool 10,000 GPM of chiller
by 1 degree Fahrenheit (F) in one hour. Figure 2 shows that
the efficiency of the system varies throughout the day and
therefore there is potential to investigate more to understand
the driving factors of such variation in cost.
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Fig. 2.

Evolution of KFG by Hour

Fig. 4.

To do so, we defined two additional measures. The first
variable, ∆T , refers to the temperature difference between the
supply chilled water and the return chilled water temperature
in degrees Fahrenheit. The second variable, NT, is equal to
AHU chilled water set point minus the return temperature of
the chilled water. The set point for the AHU is equal to 55.1F.
This measure gives an indication of how much chilled water
was consumed by the buildings to estimate the overproduction
of chilled water.

Heat consumption in a building

that opening a door for a few seconds results in an important
heat exchange between the building and the outside air. This
opening of doors is caused by human activity. Therefore,
there is a potential for energy minimization by modeling and
predicting human activity and its interaction with the building.
This model can be incorporated with an airflow monitoring
and control system to minimize the losses caused by opening
doors.
IV. S IMULATION
The goal of this simulation is to monitor and control the
airflow speed which is the main consumer of chilled water
and steam. By optimizing the airflow, we optmimize the
consumption of chilled water and steam and this affects not
only the buildings but also the central plant efficiency. The
following simulation is used to study the airflow system in a
typical room. It adjusts to the user’s requested temperature by
taking into account his/her request and therefore makes the
room more comfortable while consuming less energy.
A. Scenario

Fig. 3.

When the user feels that it is too warm for him/her, the
user requests the system to change target temperature and
specifies how long the system has to achieve it. By getting
these two parameters, the system adjusts the damper angle to
allow more/less air to flow to meet the target temperature by
the required time.

Evolution of KFG as a factor of NT and ∆T

From Figure 3, it shows that KFG is affected by ∆T and
NT. This means that the more the heat is used for the same
quantity of water, the more efficient it is. Also, the closer the
chilled water return temperature is, the more efficient it is.

B. System requirements
The system has a set of requirements that drive its architecture and design, as per the following
• Achieve target temperature within the required time
• Keep that temperature constant unless changed
• Account for change in occupancy that will impact achieving the target within allocate time
The model is designed to take into consideration that once
a quantity of air is determined, the system should always
recompute it when the occupancy of the room changes as more
people can come/leave the room, adding/removing more heat
and therefore deviate the system from its objective.
The system therefore works as follows: Once the target
temperature and time are received from the user, the system

B. Building Optimization
The buildings are the main consumers of chilled water
and steam, they use them to cool/heat the air and to remove
humidity. There are many parameters that affect the production
of air. A study was conducted to estimate the variables that
affect the consumption. The chosen parameters are the state
of doors, human presence and machines used.
Figure 4 shows how these variables consume heat when the
outside temperature varies. It is clear that the doors’ states
represent the major consumers of heat in a building. The fact
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within the required time. It is based on thermodynamic laws
and most importantly the law of conversation of energy.

senses the temperature of the room, the temperature of the
air flowing to the room, computes the angle of the damper
using the model to compute the next minute’s partial objective
and adjusts the shed to that angle. After each time period,
the temperature is expected to be at a certain level, so the
system senses the temperature of the room to see if the partial
objective has been met. Afterwards, it recomputes the angle
using the model to take into consideration the error arising
from the previous partial objective.

V. R ESULTS
The purpose of this section is to test whether the model
will make it possible to reach the goal of customizing the
temperature to the users’ needs and be able to increase both
user’s comfort and energy efficiency. To do so, a program
was built that simulates the change of temperature by adding
and removing people. It also simulates internal room heat
that changes with time (e.g. computers, heat from windows...).
Afterwards, the model was added to the program to see if it
can successfully meet the user’s target within the target time.

C. Model
The model counts on the following assumptions and parameters some of which are set as constant in the system and do
not need to be sensed and others that change and must be
sensed every time step in order for the system to achieve the
objective, as follows
1) Assumptions: The model assumes some things are constant to keep it simple. The system assumes the pressure inside
the room is the same for every room and does not change
throughout time. Also, it assumes that the speed of air, vout
is the same as the speed of the air flowing to the room. In
reality, the direction of the air flow vector affects the speed.
To keep it simple, we assume these parameters.
2) Parameters:
• Target temperature Ktarget : This is the input temperature
provided by the user. It is the target temperature to be
achieved by the system.
• Time ∆ttarget : This is the input time in minutes provided
by the user. It is the time required to meet the target
temperature.
• Output α: This is the angle of the damper that is computed by the model. It is what defines the quantity of air
flowing to the system.
• Inside temperature Kin : This is the inside room temperature. It is sensed by the sensor node within the room. It
is sensed every minute. It is also used to check the error
in meeting the partial target.
• Outside Air temperature Kout : This is the temperature of
the air in the duct and it is sensed every minute because
it can be changed by the air-handling unit and it taken
into account to compute α.
• Outside air Speed vout : This is the speed of the air
flowing in the duct.
• Volume of the room: Vroom : This parameter represents
the volume of the room.
3
• Air density ρ = 1.225Kg/m
2
• Surface of damper l : This is the surface of the damper
that we change. The bigger the α, the bigger the surface,
the more air flows into the room.
The model we have created for this study outputs the angle
α that is computed using the parameters above.
ρ∗V

∗(K

−K

A. Simulation
The simulation program is a Java SE [14] program that
depicts a room with users going in and out of the room and
controls the air damper to meet the target temperature within
the target time.
B. Data collection
The simulation is run assuming the following information.
The room size is 150 cubic meters. The air speed is 100 meters
per minute. The starting temperature is 24 degrees celsius. The
user asks the system to achieve a temperature of 21 degrees
Celsius within 30 minutes. In addition, people go into the room
and exit randomly. This generates heat randomly. The system
takes into consideration such possible changes to adjust to in
order to meet the target.

Fig. 5.

Java airflow simulation program

Figure 5 shows a screenshot of the Java simulation
application that simulates the room and shows the model in
action. The simulator outputs after every minute the current
temperature of the room and the angle of the damper that
will be set to meet the target given the time left. This data
was collected to evaluate the performance of the system.
C. Data interpretation
The data collected was used to compile Figure 6 which
shows how the angle is adjusted in order to meet the target
temperature. It also shows how the temperature changes as a
result of a change in the shed angle. Initially the temperature
is set to 24 Celsius and the user chooses 21 Celsius as target

)

room
target
in
α = tan−1 ( ∆ttarget
∗vout ∗L2 ∗(Kout −Kin ) )

This model computes the angle of the damper to ensure enough
air is flowing to the room to meet the target temperature
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[3] H. Farhangi, “The path of the smart grid,” Power and Energy Magazine,
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Fig. 6. Temperature change and angle computation to meet target temperature

that should be achieved within 30 minutes. Figure 6 shows that
the target is achieved within the required time. As a result, the
user’s comfort is increased because the target is achieved.
VI. C ONCLUSION AND FUTURE WORK
Our studies show that there is a potential for energy optimization in buildings and the central plant’s machinery. To
optimize the energy in the central plant, there is a need to
monitor the efficiency profile of every machine, how one’s
efficiency affects the others and how this efficiency profile
changes through time. therefore machine learning is the appropriate approach in this changing environment.
The buildings were shown to be subject to optimization that
will minimize the energy consumption and also increase the
users’ comfort. The airflow system simulation showed that
there is a potential for energy efficiency. As a future work, this
system will be tested in a real-world environment and serve
as an infrastructure for sensing and control. In addition, the
current model does not take into account real world parameters
such as pressure of the room, the effect of one room air needs
on the other rooms. These will be taken into consideration to
make the model more robust to these changes.
ACKNOWLEDGMENT
We would like to express our very great appreciation to
Demond Williams for his valuable recommendations as well
as his help in conducting this study. We would also like to
express deep gratitude for Tao LV who has been part of this
project, who has helped in the understanding of the system. We
would also like to thank the Central plant operators who have
spent with our group time to explain to us the the structure of
the plant as well as to helping us conduct many experiments.
We would like to thank the Central Plant in the University
of Houston for giving us the chance to work with them and
carry our project with their valuable assistance. We would
like to also thank the students from College of Technology
in University of Houston who spent long hours contributing
to this project by helping digitizing the data.
R EFERENCES
[1] D. of Energy. (2013) Building energy data book. [Online]. Available:
http://buildingsdatabook.eren.doe.gov
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ecosystems: A review,” Journal of Environmental Quality, vol. 9, no. 3,
pp. 333–344, 1980.

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