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A Survey of Home Energy Management Systems in Future Smart Grid Communications

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A Survey of Home Energy Management Systems in Future Smart Grid Communications

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In this paper we present a systematic review of various home energy management (HEM) schemes. Employment of home energy management programs will make the electricity consumption smarter and more efficient. Advantages of HEM include, increased savings for consumers as well as utilities, reduced peak to average ratio (PAR) and peak demand. Where there are numerous applications of smart grid technologies, home energy management is probably the most important one to be addressed. Utilities across the globe have taken various steps for efficient consumption of electricity. New pricing schemes like, Real Time Pricing (RTP), Time of Use (ToU), Inclining Block Rates (IBR), Critical Peak Pricing (CPP) etc, have been proposed for smart grid. Distributed Energy Resources (DER) (local generation) and/or home appliances coordination along with different tariff schemes lead towards efficient consumption of electricity. This work also discusses a HEM systems general architecture and various challenges in implementation of this architecture in smart grid.
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arXiv:1307.7057v1 [cs.NI] 26 Jul 2013
1
A Survey of Home Energy Management Systems
in Future Smart Grid Communications
I. Khan
1
, N. Javaid
1,2
, M. N. Ullah
1
, A. Mahmood
2
, M. U. Farooq
2
1
Dept of Electrical Engineering, COMSATS Institute of IT, Islamabad, Pakistan.
2
CAST, COMSATS Institute of IT, Islamabad, Pakistan.
Abstract—In this paper we present a systematic review of
various home energy management (HEM) schemes. Employment
of home energy management programs will make the electricity
consumption smarter and more efficient. Advantages of HEM
include, increased savings for consumers as well as utilities,
reduced peak to average ratio (PAR) and peak demand. Where
there are numerous applications of smart grid technologies, home
energy management is probably the most important one to be
addressed. Utilities across the globe have taken various steps
for efficient consumption of electricity. New pricing schemes
like, Real Time Pricing (RTP), Time of Use (ToU), Inclining
Block Rates (IBR), Critical Peak Pricing (CPP) etc, have been
proposed for smart grid. Distributed Energy Resources (DER)
(local generation) and/or home appliances coordination along
with different tariff schemes lead towards efficient consumption
of electricity. This work also discusses a HEM system’s general
architecture and various challenges in implementation of this
architecture in smart grid.
Index Terms—Smart grid, home energy management, demand
side management, optimization.
I. INTRODUCTION
E
LECTRICAL power grid is a system with some or all
of the following four capabilities, power generation,
transmission, distribution and control. Integration of advanced
Information and Communication Technologies (ICT) increases
the efficiency of the traditional grid which makes it capable
to make decisions fast and accurate. The integration of
ICT in traditional grid results in more automation, reliable
provision of electrical services, safe operation of electrical
appliances and hence an increased level of consumer
comfort [1]. The advent of smart grid has argued the proposal
of several emerging technologies in past decade from
many researchers across the globe. Smart meters, Advanced
Metering Infrastructure (AMI), bidirectional communication,
home automation and Home Area Networks (HANs) are the
technologies addressed by various researchers [2]. Traditional
power grid has been serving humanity for the last 100 years.
Population across all over the world and the dependency level
of human on electricity are continuously and exponentially
increasing phenomena. As not much change has been made
to the traditional grid to cope with the increased demand, the
ultimate result is that the traditional grid has worn out and
the idea of smart grid has evolved [3].
Distributed applications for smart grid may be found in
consumption, distribution, transmission, and generation of
electrical energy. Smart grid enhances the electricity usage
efficiency. If home appliances are equipped with sensors,
AMI may be used for load prediction of a specific area [4].
Efficient consumption of electricity proves beneficial to
us both socially and economically. The employment of
HEM systems in a residential area reduces energy bills
for consumers and peak demand. With a normal demand
in peak hours the utilities are able to provide power from
base plants and hence contribution of Green House Gases
(GHG) is less towards environmental pollution. In [5], a
power quality monitoring strategy has been enabled by using
sensor networks in smart grid (transmission & distribution
application). In [6], ZigBee protocol has been used for
monitoring and controlling of power for efficient consumption
and distribution (consumption and distribution application).
Distributed power generation option is always there in smart
grid technology, where in-home electricity can be generated
(photovoltaic, wind power), use the required energy locally
and sale spare power back to utility.
The load demand curve in traditional grid, where flat pricing
rates are active, shows that load demand is comparatively
high during peak periods when compared to off-peak. So
the utilities are not able to provide such high power from
their base plants (Hydal power stations) and they have to
compulsively switch on their peaker plants (thermal power
plants) for which the power generation costs and emission
of GHG,s are very high as compared to base plants. The
originally inelastic load demand curve needs to be altered
to reduce peak load demand, energy cost and emission
of GHGs. A HEM system in smart grid enables Demand
Response (DR) and Demand Side Management (DSM)
programs. DR programs help in managing and altering
electricity consumption on electricity supply basis. Whereas
DSM programs are related to planning, implementation and
evaluation policies and techniques which are formulated to
alter the electricity usage of consumers. Different optimization
methods, protocols and standards have been proposed for
efficient coordination of domestic appliances and DER to
reduce peak load and energy usage charges. A continuous
work in this regard is underway across the globe, at academic,
industrial and at government level.
Two sorts of HEM schemes are discussed in this paper i.e.
one is communication based and the other is optimization
based. The HEM schemes are combined with different pricing
schemes in order to make the scheme more efficient. For
example in [7], a day ahead pricing has been used in a HEM
2
scheme to minimize the electricity charges of a consumer.
II. HOME ENERGY MANAGEMENT AND MONETARY COST
MINIMIZATION
Energy management is a term, which has been applied with
various meanings in different situations. It is a broader term
but our concern is with energy saving in business, public
sector/ government organizations and homes. The process of
observing, controlling and conserving electricity usage in an
organization/ building is termed as energy management or
home energy management [3]. It has been reported that 40%
of the global power consumption takes place inside residential
buildings [8]. In the case of smart grid the consumers are able
to generate local energy (in-home energy) from distributive
generation units. Also there is an opportunity for various
pricing schemes, so the need for HEM programs has been
addressed by many researchers. Previous work has proved that
energy management programs with feed-in (local generation)
give increased savings as compared to without feed-in. Fig.
1 [2], shows the savings for both feed-in and without feed-in
systems.
Fig. 1. Appliance electricity costs with feed in
Various pricing schemes have been employed for billing
purposes by the distribution companies to make an energy
management scheme more efficient. The pricing schemes
proposed so far for smart grid are RTP, ToU pricing scheme,
CPP, day ahead pricing (DAP), etc. In RTP scheme, consumer
is informed about the pricing rates at hourly basis as the rates
may change hourly. In ToU pricing scheme a consumer is
charged least during off-peak, less during mid-peak and more
during peak periods.
Base plants which are usually categorized as renewable
energy sources like hydro power plants provide power to
base loads. For the peak periods when the consumer demand
climbs rapidly the utilities bring their peaker plants online
to maintain balance between load and demand. The peaker
plants are run by diesel generators hence the generation costs
are comparatively high. Also, emission of high amount of
GHG is associated with the operation of these plants [2]. The
overall effect of this process is, increase in electricity prices
and global environmental problems. The energy management
algorithms can shift load from peak periods to off-peak periods
to avoid the services of peaker plants and hence reduce the
generation cost and emission of GHG. In this regard, the
previous work shows that the objective of avoiding the services
of peaker plants can also be achieved by scheduling the DER
i.e. scheduling the DER by optimizing an objective function.
DSM concept was first introduced in the late 1970s [9].
A DSM program contributes in reduced emissions of GHG,
reliable provision of electricity and reducing the energy cost.
Traditional grid has DSM programs for consumers like in-
dustrial plants and commercial buildings; however it does
not offer any such program for residential consumers. The
reasons behind this are lack of sensors, effective automation
tools and efficient communication. Also the advantages for
several DR programs are negligibly small when compared
with its implementation costs. However in smart grid, the
set of smart meters, low cost sensors, smart loads, and the
integration of ICT has opened a window for residential energy
management programs [2]. In this regard a lot of work is in
progress for designing efficient routing protocols for WSNs to
address the issues of efficient energy utilization, delay, path
loss, interference and quality of service etc. In [10]- [12]
different energy efficient routing protocols have been proposed
by the authors.
In subsections below few of energy management schemes
are presented. The basic aim of these schemes is to reduce
peak load demand, electricity consumption charges and the
emission of GHG.
A. Optimization-Based Residential Energy Management
(OREM)
In [2] a linear programming (LP) model has been proposed
by the authors that aim on minimizing the cost of electricity
at home. The scheme assumes that a day is divided in
equal length, consecutive time slots with different prices
of electricity similar to ToU tariff. The proposed objective
function makes sure to reduce the home energy expenses by
scheduling the home appliances in appropriate time slots.
Input for LP model is the consumer requests and the model
gives optimum appliance scheduling at the output.
The objective function proposed is defined as [2]
I
X
i=1
J
X
j=1
T
X
t=1
K
X
k=1
E
i
D
i
U
t
S
ij k
t
(1)
Where
I Number of appliances
J Days
K Number of requests
T Number of time slot
3
E
i
Energy consumption of appliance ˙ı
D
i
Length of cycle of appliance ˙ı
U
t
Unit price for slot t
S
ij k
t
The ratio of the time that an appliance ˙ı runs in a
time slot ’t’ to the total length of appliance cycle.
Scheduling an appliance in an appropriate time slot may
bring a non acceptable amount of delay to the appliance
cycle and an exploded load in the low price time slots. To
tackle this problem an upper bound delay D
max
is specified
for each appliance which is less than or equal to the length
of two time slots. Mathematically
D
max
2D
i
(2)
Where D
i
denotes the operation cycle of appliance ˙ı.
B. In-Home Energy Management (iHEM)
iHEM, an energy management scheme for domestic
energy management is presented in [2]. The scheme uses
smart appliances, a central energy management unit (EMU)
and wireless sensor home area networks (WSHANs) for
communication purposes among appliances, EMU and smart
meters. iHEM uses Zigbee protocol for the implementation of
wireless sensor network, organized in cluster-tree topology.
The application is based on appliance coordination system
(ACS). Unlike OREM, the consumer’s demands are processed
in near real time in iHEM.
A consumer may turn on any appliance at any moment
on the clock irrespective of the peak hours concern and
iHEM suggest a convenient start time to the consumer.
On switching the appliance on, a START-REQ packet is
sent by the appliance to the EMU. Upon receiving the
START-REQ packet, EMU communicates with the storage
system to inquire about the available stored energy by
sending AVAIL-REQ packet. EMU also communicates with
smart meter to know about the updated prices. The storage
unit sends an AVAIL-REP packet in reply, containing the
information about the amount of stored energy. When EMU
receives the AVAIL-REP packet, it schedules a convenient
start time for the appliance according to the iHEM algorithm
and notifies it to consumer by sending a START-REP packet.
The consumer at this stage may be willing to negotiate with
EMU, through the NOTIFICATION packet. Message flow is
shown below in Fig. 2 [2], for iHEM application. 30% of the
load takes place during peak hours in the absence of energy
management programs. By employing iHEM, peak load can
be reduced up to 5% [2]. iHEM also reduces carbon emission
and energy consumption costs.
Fig. 2. Message flow in iHEM application
C. Appliance Coordination (ACORD)
In [15] ACORD scheme has been proposed to benefit
from ToU pricing and decrease energy cost. Aim of ACORD
scheme is to shift the consumer load to off-peak periods.
In-home WSNs are used for delivery of consumer requests
to EMU. The work shows that the rate of consumer
requests has a sizeable effect on energy cost reduction.
Energy consumption lowers significantly with an increase in
request rates from consumer side. Alternatively, consumers
participation in the energy management program enhances
the efficiency of the scheme. This scheme only considers the
scheduling of home appliances.
D. Optimal and Automatic Residential Energy Consumption
Scheduler
The optimization based residential load control scheme
discussed in [16], is based on simple LP computations. The
scheme is proposed for real time pricing which needs a price
predictor. The combination of price predictor and energy
consumption scheduling (ECS) device significantly lowers
the PAR in load demand for different load scenarios. The
optimization problem [16], given below is solved by using
LP techniques.
minimize
x∈X ,v
h
h∈H
H
X
h=1
v
h
+ λ
wait
H
X
h=1
X
a∈A
(δ
a
)
β
a
h
x
h
a
/E
a
a
h
X
a∈A
x
h
a
v
h
, h P,
b
h
X
a∈A
x
h
a
+ (a
h
b
h
)c
h
v
h
, h P,
ˆa
h
X
a∈A
x
h
a
v
h
, h H/P,
ˆ
b
h
X
a∈A
x
h
a
+ a
h
ˆ
b
h
c
h
v
h
, h H/P.
(3)
where, v
h
is an auxiliary variable, λ
wait
shows the importance
of waiting cost term in objective function, δ
a
is the cost of
4
waiting, α
a
is the scheduling starting time of appliance a, β
a
is the scheduling ending time of appliance a, x
h
a
is the energy
consumption scheduling vector, and E
a
is the predetermined
energy of appliance a.
The simulation results shown in Fig. 3 [16], show the
reduction in PAR by solving the optimization problem by LP
techniques. The results are satisfactory for utilities.
Fig. 3. Trends of daily PAR for a typical residential load based on DAP
adopted by IPC from 1 September to 31 December 2009
E. Appliance Coordination with Feed-In (ACORD-FI)
ACORD-FI [17], is another energy management scheme
for energy-aware smart homes. In ACORD-FI both the home
appliances and distributed energy resources are scheduled
with the purpose of reducing the energy bill and GHG [17].
ACORD-FI schedules consumer requests considering peak
hours, local energy generated and other conflicting requests.
ACORD-FI uses WSNs for communication between EMU
and appliances and smart meters.
F. Optimum Load Management (OLM) Strategy
In [18], an optimization based residential load management
strategy has been proposed. The optimization problem needs
several interests forecasting and activity scheduling by users to
form an objective function. Various interests are local power
production i.e. from solar, wind etc, load, and electricity prices
for next day. Following objective function is produced as a
result [18].
=
i=24
X
i=1
[(
n=N
X
n=1
V UA
n
(i)P DCA
n
(α
n
, i)+
k=K
X
k=1
V UEV
k
(i)P DCEV
k
(β
k
, i))
EP (i)(
n=N
X
n=1
P DCA
n
(α
n
, i)+
k=K
X
k=1
P DCEV
k
(β
k
, i)) + EP (i)(W P (i) + P V P (i))
(4)
Where
is the difference between the amount the user have paid
and the cost of obtaining the required energy from grid.
V UA
n
(i) is the ıth hour value of appliance of user n,
P DCA
n
(α
n
, i) shows the power demand of user n for ıth
hour, V U EV
k
(i) represents the user value to travel in electric
vehicle k for ıth hour,P DCEV
k
(β
k
, i)) is the power demand
of vehicle k for charging purposes in ıth hour. P V P (i)
and W P (i) are forecasted photovoltaic and wind power for
ıth hour respectively. EP (i) is the forecasted electricity
consumption prices for ıth hour. The authors proposed
heuristic optimization techniques to solve the optimization
problem due to the nonlinear nature of the function. Although
these techniques do not ensure to find global best solution
but can give fairly good solutions with low computational
time [18].Simulation results show that OLM can reduce
energy bill by 8-22% [18].
G. Decision support Tool (DsT)
Decision Support Tool (DsT) has the primary aim to help
users in making intelligent decisions during their appliances
operation. Advantages of energy management program may be
increased if DER coordination is adopted in parallel with ap-
pliance coordination. In [19], the concept of DER coordination
has been evaluated. The work has used an enhanced particle
swarm optimization (PSO) solver i.e. CPSO-R, to quantify
value added by the DER coordination. Coordination value has
been calculated first for the case when each DER is scheduled
independently and then for the case when the DER cooperates
with each other. A typical smart home case study is shown in
the Fig. 4 [19]. DsT is composed of DER scheduling algorithm
Fig. 4. A DsT smart home case study
and an energy service model. The net benefit of the consumer
is maximized by scheduling the controllable DER according
to the scheduling algorithm. And the consumer energy bill is
reduced by 16-25% [19].
5
H. Sensors and Control System
The devices included in this subsystem constitute the basic
part of HEM systems. In future smart homes, these devices
will provide benefits in the form of facilitating local power
generation, managing energy storage centers and diagnostic at
a micro-level. Besides power detector capability the sensors
are also used for sensing an environmental phenomenon
like humidity, temperature or even for inquiring the absence
or presence of people in a room. A controller is necessary
for receiving the remote control commands to control the
operation of a home appliance. Two main issues with
deployment of these devices are accuracy and compatibility.
I. Monitoring and Control Devices
Monitoring and control devices provide a visual interface
to the users. Data injected from IPMR is visible to the
consumers via an in-home display (IHD), where a user can
see their real-time consumption and electricity prices at that
time. The two challenges for monitoring and controlling
devices are user friendliness and simple integration of control
interface. Usually incorporation of too many appliance
controls on a single control panel may result in a panel with
large number of control buttons which will never be a feasible
design especially for kids and senior citizens. Similarly the
integration of appliances and devices from different vendors
and hence using different standards must be an effective and
an open research issue [3].
J. Intelligent Power Management Rostrum (IPMR)
IPMR is considered to be the heart of HEM system. The
primary purpose of integration of IPMR is the exploitation
of data from sensors, external internet (utility company)
and local environment and transfer it to the IHD for user’s
assistance. Alternatively IPMR provides home automation.
To be more specific IPMR provides three kinds of services,
power management services, context aware services and
social network services.
III. CHALLENGES FOR SMART GRID
Smart grid is smart because of the fast communication
and efficient networking capabilities. A lot of work has been
done in this regard to make smart grid to get an entry on the
stage and serve humanity everlastingly. Still there are some
challenges to be tackled before smart grid can do its job.
We present some major challenges related to smart grid in
this part of the work. These challenges will channelize the
research directions for future smart grid.
1) Interoperability: When equipments, devices or
appliances having different communication and networking
technologies can communicate effectively, interoperability
is satisfied. Different communication technologies may be
adopted by different utility companies, vendors and users [3].
Therefore it becomes essential to satisfy interoperability
so that a number of heterogeneous communication and
networking technologies could coexist in various parts of
smart grid. For example an Energy Management system may
use WiFi and ZigBee for communication purposes. A lot of
work can be done in this context.
2) Scalability: A system whose performance increases
with addition of more hardware proportionally to the number
of hardware added is said to be a scalable system. Smart grid
like traditional grid will involve a large number of users and
the number will increase every moment, hence scalability
becomes an issue. Tests for scalability made on small scale
may not be valid when used with such a huge amount of user.
One approach towards scalability problem is using sensor
networks which give comparatively good scalability results.
3) Interdisciplinary: Smart grid involves different
stakeholder (societies,organizations and systems). Hence the
research area has been an interdisciplinary in nature which
has always been a tough job. In smart grid, one can see the
integration of power systems with actuation, security, control,
communication and networking system.
4) Security and Privacy: Where there is interconnection of
two systems or networks (wired or wireless), there are issues
of security and privacy and the same is true in the case of smart
grid. Threat of cyber vulnerabilities for future smart grid is an
important issue to be tackled. The security issues in smart grid
may include accessing smart meter data in an unauthorized
fashion, electricity theft, accessing of home appliances control
by an unauthorized person or system, or attacking smart grid to
affect power continuity. An outsider can access smart metering
data and sensible information may be leaked out. This issue
is a major hinder in modernizing the traditional grid [3].
IV. CONCLUSION
On a conclusion note, in this work we have revisited the
need for home energy management for efficient usage of elec-
tricity in smart grid. Efficient consumption of electrical energy
results fruitfully in lowering peak load, reducing electricity
bills and minimizing the emission of GHG. Smart grid is
facilitated by bidirectional communication and effective home
automation hence an intelligent home energy management
system can be designed. Our work has discussed several
HEM schemes,summarized in table. 1, where different pricing
schemes have been applied to get social and economical ad-
vantages. Both optimization-based and communication-based
HEM techniques have been
evaluated. We have also discussed a general HEM system’s
architecture and the challenges that future smart grid will be
facing. We are of the hope that this work will channelize the
efforts towards a more efficient, user friendly HEM system for
future smart grid.
6
TABLE I
Comparison of Different Home Energy Management Schemes
Scheme
Name
Pricing Goal Method Communication Coverage
Peak Load
Reduction
Monthly
Bill
Reduction
OREM
ToU
Cost
minimization
LP Based
Optimization
No Local N/A 35%
iHEM
ToU
Cost
minimization
Interactive
Load
Shifting
Yes Local 40% 30%
RLC
RTP
Cost and PAR
minimization
LP Based
Optimization
No Local 22% 16-25%
OLM RTP
Cost and Con-
sumption min-
imization
Heuristic
Optimization
Techniques
No Local N/A 8-22%
DsT ToU,CPP
Scheduling
DER
PSO No Local N/A 16-25%
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Fig. 5. HEM System Architecture for Future Smart Grid
... Khan, I. & Javaid et. al. [18], It gives the insight of all the HEMS schemes. It makes electricity consumption more smart, really performance effective and reduced electricity prices for its consumers. ...
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Amaç: Araştırmanın amacı, ampute futbolcuların yeni tip koronavirüs salgınına yönelik algı ve tutumlarının çeşitli değişkenlere göre incelenmesidir. Materyal ve Metot: Çalışmaya 2020–2021 futbol sezonunda görüşü alınan 66 ampute futbolcu katılmıştır. Veri toplama araçları olarak “Kişisel Bilgi Formu” ve Artan ve ark. (2020) tarafından geliştirilen “Koronavirüs (Covid - 19) Salgınına Yönelik Algı ve Tutumları Değerlendirme Ölçeği” kullanılmıştır. Katılımcıların ölçek alt boyutları farklı değişkenlere göre sınanmıştır. Bu alt boyutlar genel algı (tehlikelilik, bulaşıcılık), nedenler algısı (komplo, çevre, inanç), kontrol algısı (makro, kişisel, kaçınılmazlık) ve kaçınma davranışları (bilişsel kaçınma, ortak alanlardan kaçınma, kişisel temastan kaçınma) olarak belirtilmiştir. Araştırmaya katılan ampute futbolculardan elde edilen veriler SPSS-22 paket program ile analiz edilmiştir. verilerin normal dağıldığı tespit edilmiştir. Bu nedenle iki gruplu karşılaştırmalar için bağımsız örneklemler için T-Testi ikiden fazla gruplar için ANOVA testi uygulanmış ve farklılığın hangi düzeyde olduğunu belirlemek için Bonferoni testi uygulanmıştır. Bulgular: Yapılan istatiksel analizler sonucunda; evli olan katılımcıların algı ve tutumlarının daha yüksek olduğu tespit edilmiştir. Fakat nedenler algısı (komplo) ve kaçınma davranışları (kişisel temastan kaçınma) (p<0.05) hariç diğer parametrelerde anlamlı farklılık tespit edilmemiştir (p>0.05). Covid-19 geçiren ve geçirmeyen katılımcılar incelendiğinde nedenler algısı (çevre) boyutunda anlamlı farklılık tespit edilmiştir (p<0.05). Katılımcılar doğuştan ve sonradan ampute olanlar olarak değerlendirildiğinde; sonradan ampute olan katılımcıların algı ve tutumlarının tüm alt boyutlarda yüksek olduğu tespit edilmiştir. Sonradan ampute olan katılımcıların genel algı (tehlikelilik, bulaşıcılık), nedenler algısı (komplo) ve kontrol algısı (kaçınılmaz) alt boyutları sonuçlarına göre doğuştan ampute olan katılımcılara göre anlamlı derecede farklılaştığı tespit edilmiştir (p<0.05). Katılımcılar eğitim durumuna göre karşılaştırıldığında; genel algı (tehlike, bulaşıcılık), nedenler algısı (komplo), kontrol algısı (makro, kişisel), kaçınma davranışları (bilişsel kaçınma, ortak alanlardan kaçınma, kişisel temastan kaçınma) alt boyutlarında farklılık tespit edilmiştir. Yapılan post hoch testi sonrasında eğitim seviyesi yükseldikçe covid-19 algı ve tutumlarının da yükseldiği tespit edilmiştir. Sonuç: Araştırma sonuçlarına göre, ampute futbolcuların Covid-19 pandemisine yönelik algı ve tutumlarının yüksek olduğu tespit edilmiştir. Ampute futbolcular gibi diğer dezavantajlı gruplara yönelik rehabilitasyon çalışmalarının artırılması önerilmektedir. Covid-19 ve benzeri küresel nedenli problemlerde bilgilendirme çalışmalarına önem verilmelidir.
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Amaç: Covid-19 olarak bilinen koronavirüs Çin’in Wuhan kentinden başlayıp tüm dünyayı etkisi altına almıştır. Bu salgın tüm tedbirlere rağmen ülkemizi de etkisi altına almış ve hemen hemen her bireyimizi etkilemiştir. Ampute futbolcular da bu süreçten fiziksel ve psikolojik olarak etkilenmiştir. Ampute futbol liginin uzun süre ertelenmesi ve covid-19 döneminde kısa ve uzun süreli kapanmalar pandeminin ampute futbolcularda olumsuz etkilerini artırmıştır. Bu çalışmanın amacı Ampute futbolcuların covid-19 salgınına yönelik korkularını tespit edip, sporcu ve antrenör ilişkilerine etkisini incelemektir. Yöntem: Araştırma bir betimsel bir araştırmadır. Çalışmaya 2020-2021 Ampute Süper Liginde oynayan farklı takımlardan yaşları 14-43 arasında değişen 66 ampute (33 doğuştan engelli, 33 sonradan engelli) futbolcu katılmıştır. Katılımcıların demografik bilgileri alınmış ve katılımcılara ‘Covid-19 Korkusu Ölçeği’ ve ‘Sporcu- Antrenör İlişkisi Envanteri’ uygulanmıştır. Ölçekler ampute oluş zamanı, medeni durum, covid-19 hastalığına yakalanıp yakalanmama durumuna ve eğitim durumuna göre incelenmiştir. Normallik analizi; katılımcı sayısı>50 olduğundan Komogrov-Smirnov Testi ile sınanmış ve verilerin normal dağıldığı tespit edilmiştir. İkili grup karşılaştırmaları için Bağımsız Örneklem T Testi, çoklu grup karşılaştırmaları için ANOVA testi uygulanmıştır. Bulgular: Yapılan istatiksel analizler sonucunda ampute oluş zamanına göre Covid-19 korkusu arasında anlamlı farklılık bulunmazken (p>0.05), sporcu-antrenör ilişkisi bakımından sonradan ampute olanlar aleyhine anlamlı farklılık tespit edilmiştir (p<0.05). Medeni duruma göre karşılaştırıldığında evli ve bekar katılımcıların Covid-19 korkusu arasında bekar katılımcılar aleyhine anlamlı farklılık varken (p<0.05), sporcu-antrenör ilişkisi arasında anlamlı farklılık tespit edilmemiştir (p>0.05). Covid-19 hastalığına yakalanıp yakalanmama durumuna göre incelendiğinde Covid-19 korkusu ve sporcu-antrenör ilişkisi bakımından anlamlı farklılık bulunmamıştır (p>0.05). Katılımcılar eğitim durumuna göre incelendiğinde Covid-19 korkusu ve sporcu-antrenör ilişkisi bakımından ortalama düzeyinde farklılaşsa da bu fark anlamlı düzeyde bulunmamıştır (p>0.05). Sonuç: Araştırma sonuçlarına göre; ampute futbolculardan bazılarının covid-19 sürecinden korktuğu ve bu korku sonrasında sporcu-antrenör ilişkilerinin de etkilendiği tespit edilmiştir. Pandemi gibi etki derecesi geniş olaylarda yaşlı, çocuk veya engelliler gibi dezavantajlı gruplara yönelik rehabilitasyon hizmetlerinin artması gerektiği önerilmektedir.
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