A Smart Heating System for Energy Management using an Enhanced Kinetic User Interface

IPASJ International Journal of Electrical Engineering (IIJEE)
Web Site: http://www.ipasj.org/IIJEE/IIJEE.htm
A Publisher for Research Motivation........ Email: editoriijee@ipasj.org
Volume 3, Issue 12, December 2015 ISSN 2321-600X
Volume 3, Issue 12, December 2015 Page 1
ABSTRACT
Improving energy efficiency of buildings is becoming increasingly critical to reduce energy consumption and to solve the
environmental crisis while ensuring thermal comfort. Heaters are among the appliances that consume a significant amount of
energy. This paper introduces a way to optimize the energy consumption of heating systems by adjusting the room temperature
and minimizing the heating periods while keeping the comfort of the occupants. The kinetic user interface combines motion
awareness and activity recognition to regulate the temperature depending on the type of activity the user is carrying out.
However, changing the temperature is a slow process. Starting to adjust the temperature exactly when the user starts an activity
might be too late and might cause physical discomfort. Consequently, we propose an enhanced kinetic user interface that takes
into account not only the user’s current activity but also the time the room needs to heat up or cool down.
Keywords: Smart heater, Temperature adjustment, Energy consumption, Kinetic User Interface
1.INTRODUCTION
One of the main sources of residential energy consumption and the biggest contributors to increasingly high electricity
bills are heating systems. In Switzerland for instance, the energy spent on heating can reach 70 % of the total
consumption in residential areas [1]. Solutions to this excessive energy consumption that incorporate efficient energy
control exist. An example would be the RWE Smart Home and Aprilaire. These solutions support interfaces to allow
occupants to configure the thermostat themselves according to their individual preferences [2]. Moreover, several
research projects have been conducted to equip buildings with smart systems that control and monitor the users’
heating habits, thus reducing their energy consumption. Among the most innovative technologies related to smart
heating, there is the Intelligent Building Management System that was proposed to control the indoor temperature by
getting the input data from user interfaces (voice or keypad) and then adjusting the heating using a thermostat [3]. It is
based on explicit user feedback, which can be inaccurate and can even increase the energy consumption. Moreover, this
system does not consider the user’s complex activities and occupancy patterns. The Zoned Comfort Control system is
slightly more sophisticated since it allocates a different temperature for each designated area of the building, however it
does not consider the occupancy patterns [4]. The Telkonet Intelligent Energy Management system goes a step further
and is capable of adjusting the temperature depending on the occupancy rate of the space [5]. However, this system
requires the users to set their preferences, which makes it not totally autonomous.
Technologies that take into account the activity patterns of the users exist and are based on kinetic awareness
implemented using Kinetic User Interface (KUI). For instance, the MavHome joint research project of Washington
State University and the University of Texas uses the observed sensor data to create context profiles of the users and
adjust the temperature [6]. The smart heaters that use KUI technology are the main focus of this paper. They
incorporate motion awareness and activity recognition to adjust the indoor temperature automatically. For instance, if a
person is physically active, we estimate that the temperature can be reduced by 1 or 2 °C with no essential negative
notice by the user because the heat of a body in action compensates for the lowered temperature. On the other hand, if a
person is quietly reading, more heat will be needed for the user to feel comfortable. This suggested smart heating
system integrates the time needed for the room to heat up or cool down. The optimal time to switch the heater on and
off is determined through Simulink simulations. In addition, this paper draws a comparison between the energy
consumption in a room with a regular heater and a room with a KUI heater. The general architecture of this enhanced
kinetic user interface system is detailed in the following sections. Sec. II presents the model of the user’s activities. Sec.
III describes the suggested system design and the corresponding architecture. Simulation results are presented in Sec.
IV followed by a draft cost study in Sec. V. Finally, Sec. VI concludes the paper.
A Smart Heating System for Energy
Management using an Enhanced Kinetic User
Interface
Mohamed Amine Rafkaoui1
, Nabil Birouk1
, Ghita Lahlou1
, Yassine Salih Alj1
1
School of Science and Engineering Al Akhawayn University in Ifrane Ifrane, Morocco

2.USER’S ACTIVITY
To be able to efficiently model a smart heating system, the user’s activity needs to be properly defined and
comprehended. Indeed, the fluctuations of temperature in a room can be understood by the different types of activities
that are undertaken. Usually, a room is at a basic temperature with little to no fluctuations when it is empty. However,
depending on the number of occupants in the room, the temperature can fluctuate if any activities are happening,
making it possible to reduce temperatures by 1 to 2 °C without having occupants noticing a change [7]. This change in
temperature reduces the energy consumption. The smart heating system would use KUI to implement this temperature
change and set a maximum temperature that would prevent the user from opening the windows causing energy loss.
The challenge in this situation is to predict people’s behavior while optimizing their comfort level using the KUI
technology. The smart heating system should be able to deduce the temperature levels needed in specific areas of a
household depending on multiple factors. Although the predictions may not be suitable for all situations, they should be
close enough to make the system work. For instance, the system would need to consider the age of the occupants, since
the younger generations may be more active, resulting in higher changes in temperature. The system would also have to
cover multi activity rooms. A bedroom could be used for multiple activities, making the system rely on more elements
such as the location of the people and their activities, and predict their future activities and locations.
3.SYSTEM DESIGN AND MODEL
3.1 Architecture
A smart heating system consists of a variety of embedded sensors, actuators and computing units. The objective is to
simply understand environment contexts, which include activity, behavior and time awareness. Based on this
contextual information, the smart heater adjusts the room temperature and minimizes the heating periods while
maintaining the comfort of the occupants. However, integrating a large number of devices may result in a complicated
system, in which different components may have conflicting inputs and output. The solution that is considered in this
paper is a multi-agent technology for the smart heating design. An agent is an independent hardware/software co-
operation unit that can analyze the situation and respond to stimuli according to predefined individual behaviors [8].
Each agent has its own functionalities such as sensing, acting and decision making as detailed in Table. 1.
Moreover, an agent incorporates the following characteristics: goal oriented, adaptive, mobile and self-reconfigurable.
The agents are capable of understanding a situation and adapt to changing environment through self-configuration. In
the smart heating context, the agents will adapt the room temperature depending on the user’s activities, taking into
account the time that the heaters need to heat up or cool the room down. First, the analysis of the occupants’ heating
needs is achieved through learning and contextual modeling of event data. Then, the agents identify the most suitable
action from the various options available. To model the multi-agent technology, a finite-state machine (FSM) model is
used. This machine’s components usually go through a number of states. Each state represents the status of an agent
and is associated to specific functions. A state evolves and reaches the next state by using transitions [9].
Table 1: Functions and Devices in Smart Heating System
Smart Heating System
Functions Temperature, Thermal
Management, Comfort
Sensors Temperature sensor, Motion
detector,
Actuator Heater
Computin
g Units
Microcontroller, Operation period
calculator

Decision Agent
Sensing Agent Action Agent
S1
S2S3
S1
S2S3
S1
S2S3
T1
T2
T2T2
T1T1
T3
T3
T3
Send
Request
Configure
Figure 1 Architecture of the Finite State Machine Model
For the smart heating system, there are three states: cooling down, heating up and done. There are also three transitions
between these states and three types of agents: high-speed sensing, decision and action agent. Figure 1 illustrates the
global architecture of the multi-agent technology implemented using the finite state model. Each agent incorporates
three states. The sensing agent send the input data and user’s activity pattern to the decision agent. When the action
agent receives a request from the decision agent, it will change from a given state to the next. When the desired
temperature is reached, it will change to the following state and send the feedback to the decision agent. To model this
system mathematically, a set S of three states was defined as follows: S1 is cooling down, S2 is heating up and S3 is done.
In addition, a set of transitions T is defined where T1 represents the transition between S1 and S2, T2 represents the
transition from S2 to S3, and T3 represents the transition between S3 and S1, where:
S= {S1 , S2 , S3 }, (1)
T= {T1 , T2 , T3 }, (2)
and
S ∩ T= 0, (3)
since there are no elements that belong to both sets at the same time, the intersection between the set S containing the
three states and the transition set T is equal to zero.
A consistency function is a four-tuple combination of states, transitions, input and output. The input variable IN
consists of the data collected by the sensing agent. The output variable OUT represents the choice made by the decision
agent in order to adapt the temperature.
F= {S , T , IN, OUT }. (4)
The incidence matrix W, which shows the relationship between the input and output, is given by:
W= OUT - IN, (5)
The incidence matrix W is used to find the change in the four-tuple combination function upon making a transition
from one state to another. The characteristic equation of a state transition is given by:
Mi = M0 + Ti * W, (6)

where M0 is the initial state of the state transition equation in (6) and Mi determines the action taken by the decision
agent at each state and transition such that i = 1 at S1 and T1, i = 2 at S2 and T2 , and i = 3 at S3 and T3.
If the incidence matrix’ coefficients are all less than one, the system is consistent, which means that the finite state
machine model can be used as a mathematical model for the smart heating system.
3.2 Kinetic User Interface
Heating System Manager
Activity Layer
(Action Agent)
KUI-Space Layer
(Decision Agent)
Observation Layer
(Sensing Agent)
Temperature Sensor Motion Detector
Computing Units Situation
inputs &
Occupancy
patterns
States &
Transitions
(S,T)
Situation
output
Figure 2 Kinetic user interface layers
The described mathematical model, the multi-agent technology and the finite state machine model will all serve as a
basis for building the kinetic user interface (KUI). The KUI is a system that analyzes human interaction and translates
it to commands. To manage heating in a household, a KUI uses the occupants’ activity as signals to issue different
commands. The overall system design of the heating system manager is presented in Figure 2, which consists of three
different layers: the observation layer, KUI-space layer, and activity layer. The first layer aims to collect data through
the sensing agent using a temperature sensor and a motion detector. The second layer’s goal is to analyze the given
data using a computing unit to decide which state to apply based on the situation inputs and occupancy patterns. The
activity layer, which represents the action agent in the finite state machine model, implements the temperature change.
The data is filtered depending on the amount of time people spend in each specific area. If a person moves rapidly from
one zone to another, the data is ignored since it does not represent an actual need for change in temperature. The only
data that is considered is from subjects remaining in a specific zone for a long period, making it easier to assess the
need for temperature change. The system collects data to set up customized modes depending on the subject’s itinerary.
For example, if someone is lying in a room reading a book and leaves the room to answer the door, the system would
not change temperature. The kinetic user interface collects data from the surrounding environment and accommodates
the needs of the user in order to minimize the consumption of energy to the lowest possible point.
4.SIMULATION RESULTS
This section attempts to show that the KUI system can achieve energy savings compared to a standard heating
thermostat [10]. This is done by considering an experimental scenario where we compare between the energy
consumption of student’s room in Al Akhawayn University, Ifrane, Morocco that uses the kinetic user interface and a
room which does not use this technology. Al Akhawayn University in Ifrane is located in the Atlas Mountains at 1600
m of altitude. The use of heaters in dorms and building is extensive since the temperature can fall below zero degrees
during fall and winter seasons. The electricity consumed by the heaters costs a lot to the university.
4.1 Scenario
In order to assess the effectiveness of the system, we used Simulink to simulate the heating and cooling of a room. We
considered a thermal model of a university student’s room with a dimension of 7 m x 7 m x 3 m with a resulting
volume of 147 m³. It has 2 windows with a total surface area of 2 m². The walls are insulated with glass wool that has a
thermal conductivity of k= 0.0.38 W/m/C [11]. The glass of the windows is 0.02 m thick and has a thermal
conductivity k=0.78 W/m/C [12]. The density of air is 1 kg/m³ in Ifrane at an altitude of 1600 meters. The air exiting

the heater has a constant temperature of 60 °C and the air flow rate is 600 kg/hours (166 L/s). We assume that all
electric energy is transformed into heat energy. For this experiment, we monitor the temperature inside and outside the
room, the presence of the user, and the heat cost for a time period of 24 hours. The user is considered to be inside the
room for 5 hours and then leaves for another 5 hours in a periodic fashion in order to simulate the kinetic awareness of
the system.
4.2 Thermal Model of the Room
Figure 3 Thermal model without KUI
The thermal model shown in Figure 3 takes into account all the variables mentioned in the scenario, and calculates the
equivalent thermal resistance of the house and other variables. The thermal model is composed of subsystems such as
the set point, the thermostat, the heater and the cost calculator. The set point is the desired temperature inside the room
and is set to 21 °C. The thermostat takes as an input the set point and allows fluctuation of 2.8 °C above or below the
desired room temperature (18.2 °C minimum and 23.8 °C maximum). If the temperature drops below 18.2 °C, the
thermostat turns on the heater. The heater subsystem has a constant air flow rate and a constant air temperature. It
takes as an input the thermostat signal to turn the heater on or off. The heat flow in the room is expressed by the
following equation:
where is the heat flow from the heater into the room, c is the heat capacity of air , is the mass flow rate through
heater (kg/h).
The house subsystem calculates room temperature variations. It takes as inputs heat flow from the heater and calculates
losses to the environment. Heat losses and temperature time derivative are expressed by:
where is the mass of air inside the house, is the equivalent thermal resistance of the house
Figure 4 Thermostat subsystem

The cost calculator integrates the heat flow over time and multiplies it by the energy cost which is 0.09 $/kWh. The
outside temperature is modeled by a sinusoidal curve such that it varies by 9 °C taking 10 °C to be the average outside
temperature with a minimum of 1 °C and a maximum of 19 °C. To implement kinetic awareness, we use a pulse
generator that gives an output of 0 or 1 every 5 hours to the thermostat subsystem shown in Figure 4. When the user is
not present, an output of 0 is given to the thermostat which contains a logic multiplier and as a result, the thermostat
gives a 0 signal (off) to the heater. In order to predict the user’s behavior, we use another pulse generator that gives a 0
or a 1 signal, every 5 hours with a negative phase shift of 30 minute such that the 1 signal is sent 30 minutes before the
ON signal of the kinetic awareness. To make sure our thermostat always sends an ON signal to the heater when the
user is here and 30 minutes before, we combine both signals (kinetic awareness and predictive behavior) in an OR gate.
Whenever one of the pulses is a 1, the thermostat receives the enabling 1 signal.
4.3. Results
4.3.1. Thermal Model without Kinetic Awareness
The thermal model with no predictive behavior or kinetic awareness discussed previously has the thermostat always on
regardless of the occupancy of the room or any prediction. Figure 5 shows the values of the heat cost, indoor and
outdoor temperature, and the pulses of the kinetic awareness and predictive behavior during 24 hours. We can see from
the indoor and outdoor temperature values that the thermostat keeps the heater on during the whole 24 hours. When
temperature inside reaches 23.8 °C, the heater is turned off and turned on when it reaches 18.2 °C. The outside
temperature attains a peak temperature of 19 °C at around midday. We note that the cooling of the room is faster when
the outside temperature is low. Since the system doesn’t take into account the kinetic awareness and predictive behavior
pulses, there is no relation between the signal pulses and the indoor temperature. The heating cost calculated is 2.6$ for
24 hours which equals a total energy consumption of 28.9 kW.
0
5
10
15
20
25
30
35
40
HeatCost
0
5
10
15
20
25
Indoor&Outdoortemperature
0
0.5
1
Kinetic awareness
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
0
0.5
1
Predict behavior
HeatCost
Tindoors
Toutdoors
Kinetic awareness
Predictbehavior
Figure 5 Results of the thermal model without KUI

4.3.2. Thermal Model with Kinetic Awareness
Figure 6 Thermal model with KUI
The thermal model in Figure 6 is using the kinetic awareness and predictive behavior pulses as an input for the
thermostat. Figure 7 illustrates the results of the kinetic awareness and predictive behavior. In this system model, the
thermostat takes as an input the kinetic awareness and predictive behavior pulses to make sure the heater is on 30
minutes before the user enters and turns off after he leaves. The presence of the user is modeled by a pulsating signal
that changes from 0 to 1 every 5 hours. The prediction of the user’s presence is modeled using a pulsating signal with
a 0.5 hours negative phase shift so that the signal is 1 half an hour before the kinetic awareness model. When we
compare the temperature graph with the predictive behavior and kinetic awareness pulses, we can clearly see that the
heater is turned on 30 minutes before the user is expected to enter and turned off when he leaves. The heating cost
values shows clearly that after including the kinetic awareness and predictive behavior in our system, the heating cost
decreased from 2.6 $ to 1.8 $ for 24 hours which is equal to a decrease in energy consumption of 8.9 kW (from 28.9
kW to 20 kW). The system decreases heating cost by 30.8 %.
0
5
10
15
20
25
30
35
40
HeatCost
0
5
10
15
20
25
30
Indoor&Outdoor temperature
0
0.5
1
Kinetic awareness
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
0
0.5
1
Predict behavior
HeatCost
Tindoors
Toutdoors
Kinetic awareness
Predict behavior
Figure 7 Results of the thermal model with KUI

5.COST ANALYSIS
Beside computer simulations, a draft cost analysis is considered to determine the feasibility of the system. The cost of
the energy saved is considered in addition to the required budget for the different components needed for this smart
heating system.
Table 2 Energy saving cost
Smart Heating System using the Kinetic User
Interface
Time System
without
KUI and
Predictive
Behavior
($)
System
with KUI
and
Predictive
Behavior
($)
Money
Saved ($)
24 hours 2.6 1.8 0.8
1 month 78 54 24
6 months 468 324 144
The energy saving cost is estimated to be 144 $ for a period of six months as summarized in Table 2. The required
budget could be computed by summing the cost of the different components. The MCP9808 temperature sensor
required by the system has a typical precision of ±0.25° C over a large range of temperatures (-40°C to +125°C) with
an average cost of 4.95 $ [13]. This high accuracy sensor would allow us to monitor precisely the temperature inside a
room and let the smart heating system act accordingly. The PIR motion sensor uses passive infrared technology to
detect the presence of the user. It detects the infrared light emitted by human bodies and transforms the infrared
radiation into an electric signal. This motion sensor is arduino compatible and has a range of 7m and a detection angle
of 100° [14]. Two motion sensors would be enough to cover all the area of a 7*7 m room. An arduino will also be used
as a microcontroller and an operation period calculator and has an average cost of 25 $ [15]. Table 3 summarizes the
cost of the different parts.
Table 3 System cost
Parts Quantity Unit
price ($)
Total
price ($)
PIR Motion
Sensor
2 4.90 9.8
MCP9808
Temperature
Sensor
1 4.95 4.95
Arduino
Microcontroller
1 25 25
Total 39.75
The total cost of the system if implemented on an Arduino microcontroller would approximate 39.75 $. We notice that
we can save 24 $ per month using kinetic awareness and behavior prediction. If we consider the initial investment to be
39.75 $, we can have a return on investment during the second month. The return on investment for a semester can be
estimated to be 144-39.75= 104.25 $. The system is thus financially viable and would constitute a good investment for
the users.
6.CONCLUSION
In this paper, a smart heating system using the kinetic user interface is proposed for energy management. The overall
system model and architecture is presented followed by the system design. The user’s activity and motion pattern are
used as an additional parameter to adapt the temperature in a given room by minimizing the heating period while
keeping the comfort of the occupant. The system is based on a multi-agent technology adapted to the kinetic user
interface to sense, detect and take action. The effectiveness of the smart heating system is assessed using simulation.
The results obtained demonstrates that energy is saved in a dorm room setup. The cost of the related smart heater is
approximated to 39.75 $, which makes it affordable. Future work includes the implementation of a prototype to test the
system under different conditions.

References
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AUTHORS
Mohamed Amine Rafkaoui is a General Engineering student at Al Akhawayn University in Ifrane
(AUI), Ifrane, Morocco. He participated in an exchange program at Worcester Polytechnic Institute,
Massachusetts, USA. He was the president of the AUI Environmental Club and the vice president of
the AUI German Club. He is also a member of the AUI Honors Program and a mentor at the center for
Learning Excellence. His research interests include smart grid, electric power systems and renewable
energies
Nabil Birouk is a General Engineering student at Al Akhawayn University in Ifrane (AUI), Morocco.
He was part of a summer program in the Technical University of Munich, Germany. His research
interests include mechatronic systems and renewable energies.
Ghita Lahlou is a General Engineering student at Al Akhawayn University in Ifrane (AUI), Ifrane,
Morocco. She participated in an exchange program in University of California Irvine, California,
USA. Her research interests include mechatronics systems, smart structures and economic systems,
and energy efficiency.
Yassine Salih-Alj received the Bachelor’s degree in microelectronics from the University of Quebec
at Montreal (UQAM), Montreal, Quebec, Canada, in 2001, and the Master’s degree in electrical
engineering from the École de Technologie Supérieure (ETS), Montreal, Quebec, Canada, in 2003,
and the Ph.D. degree in Telecommunications from the National Institute of Scientific Research –
Energy, Materials & Telecommunications (INRS-Telecom), Montreal, Quebec, Canada, in 2008. He served as a
research assistant at the Telebec Underground Communications Research Laboratory (LRTCS) from 2005 to 2008, and
then during 2009 as a Postdoctoral Fellow at Poly-Grames Research Center, of the École Polytechnique de Montréal,
Montreal, Quebec, Canada. He is currently working as a permanent faculty member at the School of Science and
Engineering (SSE) of Al Akhawayn University in Ifrane (AUI), Morocco. He has published over 40 publications and
has been actively involved in IEEE events for the past five years, where he chaired and served as Technical Program

Member or as distinguished reviewer for over 100 conferences. His research interests are in the areas of Wireless
Communications, Indoor Positioning, UWB (Ultra-Wideband), Digital System Implementation, GPS (Global
Positioning System) and Engineering Education.

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