Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l.
6, N
o
. 5
,
O
c
tob
e
r
201
6, p
p
. 2
274
~228
1
I
S
SN
: 208
8-8
7
0
8
,
D
O
I
:
10.115
91
/ij
ece.v6
i
5.1
101
5
2
274
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
A Non-Linear Controller for Fo
recasting the Rising Demand for
Electri
c
Vehicl
es Applicable
to In
dian Road Conditions
Poorani S
1
,
Muruga
n R
2
1
Departm
e
nt
of
Ele
c
tri
cal
and
E
l
ectron
i
cs
Eng
i
ne
ering,
Karpag
am
Univers
i
t
y
,
Co
i
m
b
atore,
India
2
Departm
e
nt
of
Ele
c
tri
cal
and
E
l
ectron
i
cs
Eng
i
ne
er
ing, Easwari
Engineer
ing College, Ch
ennai, Ind
i
a
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Apr 25, 2016
Rev
i
sed
Ju
l 28
,
20
16
Accepted Aug 16, 2016
Thes
e da
ys
lo
ad
forecas
t
i
ng is
m
u
ch m
o
re required in ord
e
r t
o
reduce
the
was
t
age of en
er
g
y
. This
pap
e
r i
s
to
implement & develop the
idea of short
term
load forec
a
s
ting b
y
using
Artific
ial Neur
al
Network, the d
e
sign of the
neural n
e
twork model, input data sele
ction and
Train
i
ng & Testing b
y
usin
g
s
hort term
load
forecas
t
i
ng wil
l
be des
c
rib
e
d i
n
paper. F
o
r th
e EV load
forecas
t
i
ng onl
y 2 variabl
e
s
are
being us
ed as
te
m
p
erature and h
u
m
i
dit
y
to
forecast the output as lo
ad. Th
is ty
pe o
f
designed ANN model will b
e
mapped b
y
usin
g historical data of
tem
p
eratu
r
e
and hum
idit
y (
t
aken from
m
e
teorologi
cal
sites), where
a
s it is
being Trained & Tested b
y
using
historical data of
loading of EV char
ging stations (Chetan maini
,Bangalor
e
)
of a parti
c
ular
area as
Coim
ba
tore to
give th
e desired result. Train
i
ng &
Testing done b
y
using large am
ount of hi
storical data of w
eath
e
r conditions
and loading da
ta
(kV). By
th
e he
lp of this m
odel
the
y
can pred
ict
their da
i
l
y
loads (nex
t d
a
y's
load)
b
y
pu
tting
histori
cal
da
ta
i
n
the
a
c
quired
a
l
gorithm
.
Keyword:
Artificial n
e
u
r
al n
e
two
r
k
Electric ve
hicle
Loa
d
forecasting
Non
lin
ear co
n
t
ro
ller
Train
i
ng
&
test
in
g
Copyright ©
201
6 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Poora
n
i S
,
Depa
rt
m
e
nt
of
El
ect
ri
cal
and
El
ect
roni
cs
E
n
gi
nee
r
i
n
g,
Karp
ag
am
Un
iv
ersity,
Co
im
b
a
to
re.
Em
a
il: drpoora
n
ieee@gm
ai
l.com
1.
INTRODUCTION
To calc
u
late the alm
o
st accurate load foreca
sting
it is
nece
ssary to
go
thro
ugh the short
ter
m
load
forecasting [1],[2] for EV chargi
ng,
loa
d
forecasting
will be de
pe
nde
nt upon
the
hourly load
forecasting, the
load ca
n be
forecasted
up t
o
1
week
ahead
in hourly
basis
,
but the m
a
in fo
c
u
s is
to
forecast the loa
d
for the
recent ne
xt da
y (as tom
o
rrow)
whic
h anal
yses & deci
de
s the dispatc
h
e
r
powe
r
flow.
Forecasting i
n
power
sy
st
em
i
s
need
ed t
o
p
r
eve
n
t
t
h
e
po
wer
sy
st
em
& powe
r
fl
o
w
set
u
p
fr
om
any
ki
nd
o
f
da
m
a
ge an
d at
t
h
e sam
e
t
i
m
e
i
t
reduces
t
h
e
wast
a
g
e
of
ene
r
gy
[
3
]
,
[
4
]
.
B
y
seei
n
g
t
h
e
gra
p
h
of
t
h
e
l
o
ads i
t
ca
n
be
d
e
scri
be
d a
n
d
g
e
t
t
h
e
i
d
ea of l
o
ad
v
a
ri
at
i
on i
n
ho
u
r
l
y
basi
s (f
or
24
h
o
u
r
s)
, t
h
e
analysis of
pea
k
loa
d
and loa
d
analysis as well is
d
o
n
e
. Po
wer l
i
n
ear
regression
m
o
d
e
l h
a
s
b
een d
e
v
e
lop
e
d
th
at
u
tilizes no
n-lin
ear t
r
an
sfo
r
m
a
tio
n
& o
t
h
e
r
statically
m
e
thodology to effectiv
ely capture the l
o
ad
variation due
to special eve
n
ts, weat
her
pattern,
devi
at
i
o
n i
n
t
h
e ot
her
weat
he
r i
n
2 y
ears
of
pr
od
uct
i
o
n us
e. It
i
s
t
o
be s
t
at
ed t
h
at
t
h
e per
f
o
r
m
a
nce of t
h
e
algorithm
will
be acce
ptable
only
in the
norm
al operating c
o
nditions
so im
provem
e
nt in
algorithm and
vari
at
i
o
n i
s
nee
d
ed
t
o
en
ha
nce
t
h
e acc
uracy
i
n
t
h
e
per
f
o
r
m
a
nce i
n
t
h
e
ra
pi
d
weat
he
r c
h
an
gi
n
g
c
o
n
d
i
t
i
o
n
s
o
r
i
n
a p
a
rticu
l
ar area where t
h
e
weath
e
r will be ch
an
g
i
n
g
rapi
d
l
y in
a sho
r
t
p
e
ri
o
d
. Few po
in
ts to
b
e
consid
ered
while forecasting.
Inpu
t and
ou
tpu
t
will b
e
of co
m
p
le
tely
n
o
n
-lin
ear relatio
nsh
i
p.
m
o
d
e
l
wi
ll
n
o
t
b
e
v
e
ry flex
ib
le for
the rapi
d cha
n
ge in the environm
ental conditions or l
o
adi
ng
param
e
ters (kV). Recently there are bei
n
g use
d
m
o
re and
m
o
re
param
e
ters in
the place of 2
or 3
param
e
te
rs [1] to calculate the for
ecaste
d load s
o
that m
o
re
precise & ac
curate res
u
lt can
be acquire
d
not only for the
l
o
ad
forecasting but als
o
in
sec
u
rity purpose
& fa
ult
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
A N
o
n Li
nea
r
C
ont
r
o
l
l
e
r f
o
r
Forec
a
st
i
n
g t
h
e Ri
si
ng
De
m
a
nd
f
o
r
El
ect
ri
c Vehi
cl
es A
ppl
i
c
abl
e
...
. (
P
o
o
r
ani
S)
2
275
di
ag
no
st
i
c
, ne
ural
net
w
o
r
k
i
s
bei
n
g
t
a
ke
n
as hel
p
i
n
g t
o
o
l
beca
use Test
ed &
T
r
ai
ne
d
so t
h
at
i
t
can
get
t
h
e
ope
rators & di
spatche
r
s confi
d
ence
.
Wo
rk
has b
een
don
e with
th
e Ch
etan
m
a
in
i d
a
ta.
To
train
in
su
ch
a way
so t
h
at it can ta
ke the
real time data
of a
pa
rt
icular place
& the
output
ca
n be forecasted.
Th
is p
a
p
e
r
p
r
esen
ts th
e im
p
r
o
v
e
d
STLF mo
d
e
lling
b
y
tak
i
ng
tem
p
eratu
r
e & hu
m
i
d
i
t
y
as
m
a
p
p
i
ng
param
e
t
e
rs. Fi
rst
sect
i
on
of t
h
i
s
pa
per a
r
e descri
bi
n
g
th
e
b
a
sic n
e
u
r
al
network
m
o
d
e
l to
g
e
t th
e fo
recasted
load
in whic
h
load has been
forecaste
d for t
h
e rece
nt ne
xt day of
AN
N [5]
m
odel has be
en done that is called
m
u
lt
i
-
l
a
y
e
red f
eed
fo
rwa
r
d n
e
ural
net
w
or
k
[3]
f
o
r t
h
i
s
m
odel
7
2
i
n
p
u
t
s
a
n
d
2
4
o
u
t
p
ut
s
are
bei
n
g t
a
ke
n. B
u
t
si
nce at
t
h
e
t
i
m
e
o
f
i
m
pl
em
entat
i
on
o
n
l
y
2
4
neu
r
ons
are
be
i
ng t
a
ken
as i
n
put
s
so
t
h
e
r
e i
s
a nee
d
t
o
red
u
ce 7
2
dat
a
(o
f
3 day
s
i
n
ho
u
r
l
y
basi
s) i
n
t
o
2
4
dat
a
(i
n h
o
u
rl
y
basi
s) by
t
a
ki
ng t
h
e
m
ean of al
l
7
2
dat
a
(
o
f 3
da
y
s
) i
n
th
e sam
e
h
o
u
r
l
y
b
a
sis. In
the
n
e
ural
n
e
two
r
k m
o
d
e
l larg
e nu
m
b
er of i
n
pu
t
s
will b
e
u
s
ed
to
redu
ce t
h
e
weigh
t
u
p
d
a
te
p
r
ob
lem
s
b
u
t
th
en also
th
e co
efficien
ts will
b
e
n
e
ed
ed to
up
d
a
te on
ce
o
r
twice a
year.
2.
INPUT VARIABLES
7
2
in
pu
ts ar
e
bein
g
u
s
ed
along
w
ith
th
e 24
hid
d
e
n
n
e
ur
on
s
& 24
ou
tpu
t
n
e
u
r
on
s bu
t in
the u
s
ed
ANN
m
odel
sam
e
num
ber of ne
ur
ons a
r
e bei
n
g
use
d
, so i
t
i
s
need t
o
red
u
ce t
h
e 7
2
i
n
p
u
t
s
i
n
t
o
24 i
n
p
u
t
s
. S
i
nce al
l
t
h
ese 72
dat
a
are of
3
d
a
y
s
(
t
oday
,
y
e
st
erd
a
y
& day
bef
o
re
y
e
st
er
day
)
.
So
m
ean
ca
n b
e
t
a
ke
n of
al
l
3 day
s
d
a
ta in
h
ourly b
a
sis to
red
u
c
e th
e 72
d
a
ta in
t
o
2
4
i
n
pu
t
d
a
ta. In
wh
ich
weath
e
r co
nd
ition
s
are
b
e
ing
used
for
mapping & loading
data wil
l
be
use
d
for
Training
&
tes
ting & fi
nal forecasting.
Pe
rform
a
nce of the input
vari
a
b
l
e
s i
n
t
h
e
A
N
N
m
odel
[
6
]
.
S
o
t
h
e
out
p
u
t
s
ca
n
be
of
v
a
ri
o
u
s
ki
n
d
s:
2.
1.
Seas
on
al
i
npu
t l
o
ad
co
nten
t
Weath
e
r ch
an
ge an
d
v
e
ry less lo
ad
will b
e
ch
ang
i
ng
, si
n
ce lo
ad
s
will b
e
ch
ang
i
ng
slowl
y
seaso
n
to
season [5]. It'
s
all about c
ooli
ng a
n
d heating loads
ove
r a y
ear pe
riod whe
n
environm
ental conditions c
h
anges
are
bei
n
g c
onsi
d
ere
d
very
l
e
ss
as c
o
m
p
are t
o
ot
he
r t
r
opi
cal
c
o
u
n
t
r
i
e
s.
sin (2
π
n. i
/
36
5)
, c
o
s
(2
π
n. i/ 36
5)
w
h
er
e
n
= (1
,2
,3
)
i
:
( i
=
1,
2,-
-
-
-
-
-
-
3
6
5
)
num
ber
of
day
s
i
n
a
n
y
ear.
2.
2.
Weather c
o
n
d
i
tion in
put
Tem
p
eratu
r
e is m
o
st sen
s
itiv
e weath
e
r
v
a
riab
le wh
ic
h affect th
e lo
ad
ing
i
n
th
e EV cars
. If th
e area
of the
data cal
culation is bei
n
g va
ried the
n
weathe
r c
o
efficients for the
forecasting will be
di
ffere
n
t t
h
an
one
anot
her
becaus
e
of their ge
ographical
and cl
im
atic conditions, s
o
there
will
be som
e
what diffe
rent im
pact on
th
e lo
ad
ing
t
h
an
th
e regu
lar i
m
p
act o
n
th
e lo
ad
i
n
g. Th
er
e
can
be sai
d
t
h
a
t
there a
r
e t
w
o types
of tem
p
erature
vari
a
b
l
e
s - di
re
ct
and i
ndi
rect
.
So di
rect te
mperat
ure va
riable
will work area to area
whe
r
eas the te
m
p
erature
wh
ich
affect th
e lo
ad
i
n
g
o
n
th
e syste
m
lev
e
l can
b
e
said
as in
d
i
rect tem
p
eratu
r
e, in
th
e EV lev
e
l it is th
e
t
e
m
p
erat
ure
o
f
t
h
e m
o
t
o
r
pa
rt
s an
d
ot
he
r
de
vi
ces
of t
h
e
ve
hi
cl
e. S
o
t
o
c
o
nsi
d
e
r
t
h
e
t
e
m
p
erat
ure
o
f
t
h
e
ve
hi
cl
e
te
m
p
eratu
r
e
o
f
all th
e d
e
v
i
ces sho
u
l
d
b
e
tak
e
n care
(th
a
t will also
b
e
in
hou
rly b
a
sis). In
th
e m
o
d
e
lled
d
i
agram
tak
i
n
g
th
e env
i
ro
nmen
tal te
m
p
erature into c
o
nside
r
ation t
h
er
e can be s
een that how
m
u
ch
te
m
p
erature
differe
n
ce is t
h
ere
in the
system
.
It has
been
see
n
that the
r
e is
a vast effect of te
m
p
erature i
n
the loadi
n
g wh
ich
m
ean
s lo
ad
ing
is v
e
ry
m
u
ch
sen
s
itiv
e to
th
e tem
p
eratu
r
e ch
an
g
e
. No
n
lin
ea
r relatio
n
s
h
i
p
h
a
s b
een
seen
between
lo
ad
ing
and
te
m
p
erature
of the E
V
ca
rs. A ce
rtain limit of hum
i
d
clim
ate increases the tem
p
erature
and
vi
seversa.
Loa
d
i
n
g i
n
p
u
t
s
are bei
n
g use
d
o
f
To
day
,
Y
e
st
erday
an
d D
a
y
befo
re y
e
st
erday
,
t
h
e l
o
ad
i
ng d
a
t
a
of ea
r
l
y
past
is b
e
ing
u
s
ed
i
n
th
is v
a
riab
le.
Ot
he
r i
n
p
u
t
va
ri
abl
e
s can al
so be use
d
[
5
]
for m
a
ppi
n
g
pu
rp
ose an
d t
o
gi
ve t
h
e l
o
adi
ng
dat
a
as i
npu
t
fo
r t
h
e t
r
ai
n
g
a
nd t
e
st
i
n
g as w
i
nd s
p
eed
, cl
ou
d co
ver
,
de
w p
o
i
n
t
an
d i
t
has been see
n
t
h
at
t
h
ey
have
very
l
e
ss
effect
on
E
V
l
o
adi
n
g
dat
a
(C
het
a
n m
a
i
n
i
).
So
ot
he
r
vari
a
b
l
e
s are
bei
n
g
negl
ect
e
d
t
o
av
oi
d t
h
e c
o
m
p
l
e
xi
t
y
i
n
forecasting a
n
d to ac
qui
re a
n
a
ccurate
res
u
lt.
3.
NO
NLINE
A
R
MO
DELIN
G
C
R
ITERI
A
FOR L
O
A
D
FORE
CA
STI
N
G
A
ND T
R
AIN
I
N
G
AN
D
TESTING
No
nl
i
n
ea
r
beh
a
vi
o
u
r
o
f
t
h
e
l
o
adi
n
g
ha
s
be
en
di
scuss
e
d
e
a
rl
i
e
r.
Whi
c
h i
s
bei
ng i
n
fl
ue
nced
by
t
h
e
te
m
p
erature and seasonal effe
cts, this
m
odel is so
m
e
wh
at
d
i
ffere
nt
t
h
an t
h
e earl
i
e
r pu
bl
i
s
hed
pape
rs bec
a
us
e
the m
a
in foc
u
s
of the
pape
r i
s
to explain to explai
n t
h
e ba
sic
m
odel of
ANN
base
d loa
d
forecasting sy
ste
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE
Vo
l. 6
,
N
o
. 5
,
O
c
tob
e
r
20
16
:
227
4
–
22
81
2
276
[7]. In the
neural network load
forecasting
m
odel when the out
put ne
ur
ons are m
odelled w
ith the
nonlinea
r
com
b
i
n
at
i
on of
t
h
e out
put
s
o
f
t
h
e
hi
dde
n ne
u
r
o
n
s.
(
2
)
It
can
be
gi
ven
as f
o
l
l
o
ws :
=
+
+
+
+-
--
--
--
--+
(1)
whe
r
e
=
,
,
is the ou
tpu
t
of t
h
e
hi
d
d
e
n
neu
r
on
.
,
,
streng
th
of si
g
n
a
l
fro
m
1
neu
r
on
of a layer to
all th
e
neu
r
ons
o
f
t
h
e
out
put
l
a
y
e
r
.
i
s
t
h
e t
h
res
h
ol
d
out
put
o
f
t
h
e
n
e
ur
o
n
k.
From
[1]
t
h
e
o
u
t
p
ut
of t
h
e
hi
dde
n
ne
ur
o
n
c
a
n
be
gr
o
u
p
e
d
i
n
t
o
3
g
r
ou
p:
No
n a
c
t
i
v
at
ed,
Li
nea
r
a
n
d
Saturate
d. In both non ac
tiva
t
ed and sat
u
rat
e
d ne
urons out
put will
not very change ac
cording to the
input
ch
ang
e
.
wh
ereas in
th
e lin
ear state th
e o
u
t
p
u
t
will g
r
adually ch
an
g
e
acco
rd
ing
to
th
e in
pu
t ch
ang
e
, th
is is
wh
at is h
a
pp
enin
g
i
n
th
e EV case,
o
u
t
p
u
t
will ch
an
g
e
b
y
t
h
e
ch
ang
e
i
n
the i
n
pu
t.
So
i
n
th
e lin
ear state will b
e
seen
in th
e
d
e
scrip
tion
g
i
v
e
n
belo
w:
H
.
(2)
whe
r
e
X
n
i
s
t
h
e i
n
put
f
o
r t
h
e
fi
rst
l
a
y
e
r o
f
neu
r
o
n
m
odel
(a
ft
er
t
a
ki
ng
m
ean of
t
h
e
3
day
s
dat
a
).
w
is
str
e
ng
th
of
sign
al fr
o
m
1
n
e
ur
on
o
f
a layer
t
o
all th
e
n
e
u
r
on
s
o
f
th
e
ou
tput layer
.
N
o
te: Thresho
l
d
state is
b
e
ing con
s
id
ered
even
thoug
h th
ere w
ill b
e
n
e
g
l
i
g
ib
le ch
an
g
e
in
th
e
o
u
t
p
u
t
b
y
ch
ang
i
ng
the th
resho
l
d
stat
e. So equ
a
tion
1
&
2
sho
w
s t
h
at th
e
o
u
t
p
u
t
will b
e
ch
ang
i
n
g
i
n
th
e
sam
e
o
r
d
e
r
b
y
th
e ch
ang
e
in
th
e inpu
t. So
it can
b
e
sai
d
th
at its wo
rk
i
n
g
i
n
lin
ear
state an
d
sho
w
i
n
g n
o
n
lin
ear b
e
hav
i
ou
r
.In
th
e in
itial stag
es th
e lin
ear term
s are
b
e
in
g tak
e
n on
ly wh
en th
e p
e
rfo
r
m
a
n
ce is somewh
at
d
e
terio
r
ated,
because
under
these c
o
nditions loa
d
c
h
anges with t
h
e tem
p
erature
a
n
d humid
ity in qua
dratic rate, t
h
at's why
t
h
e n
o
n
-
l
i
n
ear
i
n
p
u
t
be
havi
o
u
r
has
been t
a
ken i
n
t
o
acc
o
unt
.
So t
h
at
t
h
e i
n
fl
ue
nce
of
cool
i
n
g a
nd
heat
i
n
g
degrees of the
te
m
p
erature ca
n be see
n
in the load since the te
m
p
erature
i
s
chan
gi
n
g
day
t
o
day
i
n
bot
h
t
h
e
en
ds ,
so
it is
very essen
tial to con
s
id
er th
e each
an
d ev
e
r
y
pint
of tem
p
erature c
h
a
nge
s
o
that act
ual effect of
t
e
m
p
erat
ure ca
n be seen i
n
t
h
e l
o
adi
ng .T
h
e
n fi
nal
l
y
a set of t
r
i
g
o
n
o
m
e
tri
c
fu
nct
i
on
ha
s been t
a
ke
n t
o
gi
ve
t
h
e i
n
p
u
t
i
n
t
h
e B
ack pr
o
p
ag
at
i
on al
g
o
ri
t
h
m
& t
h
i
s
m
odel
i
s
m
odel
l
e
d t
o
pr
ovi
de 1
y
ear beha
vi
o
u
r
of t
h
e
lo
ad
ing
.
4.
NE
X
T
D
AY'S
LOA
D
MO
D
ELING
So accordi
n
g to the standa
rd m
odelling 3 da
ys data
as today, yesterday & day before yesterday are
being take
n.
In the place
of
3 days data, m
a
ny m
o
re days
or m
a
y be less days tha
n
this
can be ta
ke
n a
s
well
but for
our conve
nience
we are taking
3 days input to forecast the next
day'
s output.
So hist
orical loadi
ng
data of
pre
v
ious
days ca
n
be taken into c
onsi
d
eratio
n t
o
calculate the forecaste
d
data of t
h
e
ne
xt day.
Train
i
ng
&
Testin
g
will
b
e
do
n
e
b
y
t
h
e h
i
sto
r
ical d
a
ta
of t
h
e last year b
e
cau
se
for t
h
e co
m
p
ariso
n
purp
o
s
e
act
ual
dat
a
of t
h
e ne
xt
day
wi
l
l
be needi
ng t
h
at
i
s
avai
l
a
bl
e
i
n
t
h
e p
r
evi
o
u
s
y
ear'
s
hi
st
ori
cal
dat
a
but
t
h
e fi
nal
forecasting
data will be calc
u
lated
of t
h
is
year, since
thi
s
year's real time data of forecasted
day i
s
not
avai
l
a
bl
e s
o
w
e
nee
d
t
o
ac
qui
re i
t
by
usi
n
g
m
a
ppi
n
g
phe
n
o
m
e
non
, cal
c
u
l
a
t
i
on i
s
gi
ve
n
bel
o
w :
Let
the hourly forecast of
t
h
e next day
is pre
s
ented
by:
[
, j =
1,
2,
---------24 ]
a
n
d
forecasting error [
δ
, k
=
1
,
2, --
--
--
--
-k
;
k<2
4
]
Whe
r
e
k
i
s
t
h
e
l
a
st
ho
u
r
of
t
h
e
ne
xt
day
wi
t
h
kn
o
w
n
h
o
u
rl
y
l
o
ad
s.
So
ho
url
y
l
o
ad
f
o
rec
a
st
i
s
gi
ve
n
by
k t
h
at
i
s
ho
ur
.
δ
=
-
;
k =
1
,
2
,
--
--
--
--
k
is real tim
e lo
ad
ing
d
a
ta of t
h
e
n
e
x
t
d
a
y i
n
h
our
.
Let th
e m
a
trix
co
n
t
ains
v
a
riables i and
j
.
So th
e fun
c
tion
o
f
h
ourly
lo
ad
s
is represen
ted
as:
{fun
(
i
, j)
,
wh
er
e i =
1
,
2,
--
--
--
--
-2
4 an
d
j
=
1
,
2
,
--
--
--
--
-24
Th
is m
a
trix
co
n
t
ain
s
u
s
ed
h
i
sto
r
ical lo
ad
ing d
a
ta, th
is d
a
ta is ad
j
u
sted
o
n
ce in
a year. So
no
w the
final
forecasting has bee
n
done
by the
following
form
ula:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
A N
o
n Li
nea
r
C
ont
r
o
l
l
e
r f
o
r
Forec
a
st
i
n
g t
h
e Ri
si
ng
De
m
a
nd
f
o
r
El
ect
ri
c Vehi
cl
es A
ppl
i
c
abl
e
...
. (
P
o
o
r
ani
S)
2
277
=
+
fu
n
(i,
j).
δ
Whe
r
e
j =
1
-
-
-
-
-
-
-
-
k
,
a
n
d
i =
k+1
,
--
--
--
--
-2
4
Trai
ni
n
g
a
nd
Test
i
ng
has be
en d
o
n
e wi
t
h
t
h
e real
t
i
m
e hi
st
ori
cal
dat
a
w
h
i
c
h s
u
b
s
t
a
nt
i
a
l
l
y
im
prov
e
t
h
e A
N
N
al
g
o
r
i
t
h
m
,
whi
c
h
he
l
p
s t
h
e
di
s
p
at
c
h
ers
t
o
sen
d
a
part
i
c
ul
a
r
re
q
u
i
r
ed
am
ount
of
ener
gy
.
Figure 1.
Equi
valent
bloc
k
di
agram
for s
h
ort term
load forecasting
The following steps
a
r
e being
fo
l
l
o
we
d fo
r
t
h
e dat
a
p
r
ocess
i
ng:
4.
1.
Mappin
g
15
day
s
dat
a
o
f
t
e
m
p
erat
ure
& hum
i
d
i
t
y
of l
a
st
y
ear of any
m
ont
h (s
up
pos
e Au
g
u
st
)
and t
h
i
s
y
ear has
been
t
a
ke
n.
Mappi
ng is done
with
bot
h y
ears
data and t
r
ied
to ac
quire t
h
e loa
d
ing
data
accordi
n
g to t
h
at.
M
a
ppi
ng i
s
do
ne by
t
a
ki
ng t
e
m
p
erat
ure & h
u
m
i
di
ty
dat
a
for
fi
n
d
i
n
g t
h
e
rel
a
t
i
on
bet
w
e
e
n l
o
a
d
i
n
g dat
a
o
f
t
h
i
s
y
ear a
n
d
l
a
st
y
ear by
usi
n
g t
h
e
t
e
m
p
erat
ure
an
d
h
u
m
i
dit
y
dat
a
o
f
bot
h
of
t
h
e y
ear
s.
Si
nce we are
wo
rki
n
g wi
t
h
t
h
e ne
ural
net
w
or
k so n
o
r
m
a
lizat
i
on o
f
bot
h set
of l
o
adi
ng
dat
a
was nee
d
ed
t
h
at
i
s
p
r
evi
o
us
y
ear'
s
(1
5
d
ay
s, sam
e
ho
urs
d
a
t
a
) & t
h
i
s
y
e
a
r
'
s
(1
5day
s
, sa
m
e
ho
ur
’s
dat
a
).
4.
2.
Training & T
e
sting
Trai
ni
n
g
a
n
d t
e
st
i
ng
fr
om
l
a
st
y
ear’s
dat
a
3day
s
l
o
a
d
i
n
g
dat
a
ha
s
been
t
a
ken
f
o
r
Trai
ni
ng
&Test
i
n
g.
Mean
o
f
all
3
days lo
ad
i
n
g d
a
t
a
in
h
ourly b
a
sis is b
e
ing
calcu
late.
After calcu
latin
g
m
ean
and
redu
cing
th
e
72
d
a
ta in
24
data Back
p
r
opag
a
tio
n
Algorith
m
[8
] is b
e
in
g
ap
p
lied.
Weights are
being calc
u
lated
by use
d
Al
gorithm
a
nd t
h
en re
placed
assum
e
d weight with calcul
a
ted
weig
ht.
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEC
E
2
278
4.
3.
T
F
o
in
p
T
h
I
n
1
s
t
v
a
4.
4.
L
fo
rec
a
5.
I
p
rob
l
e
com
m
do
w
n
chall
e
lo
ad
i
n
ti
m
e
o
5.
1.
O
b
eing
E
Vo
l.
6
,
N
o
.
T
ra
inin
g
&t
e
o
r Tr
ain
i
ng
&
p
u
t
s an
d 24
o
u
h
is lo
ad
ing
d
a
n
term
ed
iate fo
r
t
and 2
nd
er
r
o
a
lu
es th
e
fore
c
L
ast forecas
t
Interm
edi
a
a
sted val
u
e h
a
I
MPLEME
N
B
y
t
a
ki
ng
e
ms
,
w
h
ic
h
m
uni
cat
i
on, a
c
lo
ad
ing
th
e d
a
e
n
g
e to
pu
t t
h
n
g
to
fo
recast
o
f im
plem
ent
a
O
n line & of
f
There wil
l
u
s
ed
fo
r Tr
a
5, Oct
o
be
r
2
0
e
st
in
g
fr
om l
a
&
Testing
3d
a
u
tpu
t
s will
b
e
a
ta is
b
eing t
r
a
r
ecasted val
u
e
o
r v
a
lu
es an
d
c
ast
e
d dat
a
f
o
r
t
ed
va
lu
e
wit
h
a
te forecaste
d
a
s b
e
en
f
oun
d
.
N
TATION A
N
data
from
(
C
has
been
r
e
c
cess the
re
a
a
t
a
by
usi
ng
m
h
e d
a
ta in
a
c
t
h
e ne
xt
d
a
y
'
s
a
t
i
on.
f
line
imple
m
l
be
2 t
y
pes
o
a
in
ing
& Test
i
0
16
:
227
4 –
2
Figur
e
a
st
y
ear
’
s da
t
y
s values
of
th
e
r
e i
n
p
r
o
g
r
a
in
ed in
a p
a
rt
i
e
has bee
n
f
o
u
weigh
tin
g
v
r
t
h
e
ne
xt
day
h
this
y
ea
r
’
s
d
d
v
a
lue fo
r n
e
N
D RESULT
C
h
e
tan m
a
in
i
)
e
so
lv
ed
. Mo
s
a
l ti
me d
a
ta
m
an-m
achine
c
ertain
an
d
c
a
s
l
o
adi
ng
da
t
a
m
enta
tion
f im
plem
ent
a
i
n
g
is called
o
22
81
e
2.
Al
go
ri
t
h
m
t
a
map
p
e
d nor
m
r
a
mmin
g
.
i
cu
lar m
o
d
e
l
o
u
nd of
th
at
w
i
t
v
al
ues has be
e
ha
s bee
n
cal
c
d
at
a
e
xt
day
wi
t
h
t
) EV chargin
s
t o
f
th
e pr
o
from
EV
s
in
terfaci
n
g
.
T
a
pabl
e ne
u
r
al
a
by
usi
n
g
hi
s
a
t
i
ons b
y
usi
n
g
o
ff lin
e im
p
l
e
m
flowchar
t
m
a
lized
lo
ad
i
n
o
f
AN
N.
t
h as
sum
e
d w
e
n calculated
c
ulated.
t
he t
r
ai
n
e
d
w
g
statio
ns &
o
bl
em
s were
s
tatio
n
s
, m
a
n
T
he
n aft
e
r t
h
e
net
w
o
r
k m
o
d
t
orical loadi
n
g
g
ne
ural
net
w
e
m
e
nt
at
i
on.
H
n
g dat
a
are
be
e
igh
t
.
an
d th
e
n
b
y
ei
g
h
t
has be
e
i
mp
l
e
me
n
t
a
t
i
o
rega
rdi
n
g
d
a
n
-m
an &
m
a
n
collection o
f
d
el
t
o
gi
ve t
h
e
g
d
a
ta.
Po
in
ts
w
or
k, t
h
e n
e
ur
a
e
r
e t
a
ki
ng hi
s
ISS
N
:
2
e
i
n
g tak
e
n,
w
usi
n
g t
h
ose
e
n
f
o
un
d a
n
d
o
n has t
o
b
a
r
a
ta acquiring
a
n-machine i
n
f
data th
ere w
e desi
re
d res
u
s
t
o
be co
nsi
d
e
a
l n
e
two
r
k
[9
s
t
o
rical data
(
2
088
-87
08
w
her
e
as 24
w
e
i
g
hting
th
en fin
a
l
e
m
u
lt
ip
le
an
d dat
a
n
terfacing,
a
s anot
he
r
u
lt for th
e
e
re
d at
the
] wh
ich
is
(
last year'
s
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEC
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A
N
o
dat
a
f
dat
a
w
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u
calle
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rog
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a
5.
2.
S
com
p
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rec
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Fig
u
E
o
n Li
nea
r
C
o
n
f
or
i
n
put
& l
a
s
w
e
r
e t
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ke
n.
W
al) data f
o
r
m
d
as on line i
m
k
i
n
g 3 d
a
ys
v
a
m
.
Offlin
e i
m
S
imulati
o
n
R
T
h
is
s
ari
s
on be
t
w
e
e
a
sted im
ple
m
e
s w
e
r
e
obtain
e
u
re 3.
Trai
n
i
n
n
tro
ller fo
r
F
o
s
t year'
s
real t
i
W
he
reas i
n
o
n
m
atching the
m
m
p
l
e
m
en
tatio
n
v
alues
in ho
u
m
p
l
e
m
en
ta
tio
n
R
es
u
l
t
s
ecti
o
n r
e
pr
e
s
e
n the real ti
m
e
nt
at
i
on w
h
i
c
e
d
b
y
using
B
S No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
n
g a
n
d t
e
st
i
n
g
dat
a
o
I
S
o
reca
sting
the
i
m
e
dat
a
of
f
o
n
lin
e im
p
l
e
m
e
m
apped val
u
e
n
. All th
e d
a
t
a
r
ly b
a
sis, the
n
with
the h
i
s
t
s
ent
the c
o
m
m
e dat
a
of t
h
e
c
h
will h
e
lp t
h
B
ack propagat
i
Tabl
e 1.
T
Co
m
p
ar
ison be
t
Ti
m
e
00:00:
00
01:00:
00
02:00:
00
03:00:
00
04:00:
00
05:00:
00
06:00:
00
07:00:
00
08:00:
00
09:00:
00
10:00:
00
11:00:
00
12:00:
00
13:00:
00
14:00:
00
15:00:
00
16:00:
00
17:00:
00
18:00:
00
19:00:
00
20:00:
00
21:00:
00
22:00:
00
23:00:
00
reul
t
with
rea
o
f
ne
xt
da
y
.
B
l
SSN
:
208
8-8
7
Rising De
ma
n
o
recasted day
o
e
nt
at
i
on t
a
k
i
n
e
. So
th
is k
i
n
d
a
will b
e
Trai
n
on
lin
e & o
ff
t
ori
cal
dat
a
&
m
pari
so
n bet
w
forecaste
d d
a
h
e dis
p
atche
r
i
on al
go
ri
t
h
m
.
T
raini
n
g
an
d
T
t
ween next day's
Next Yea
r's Re
a
Data
6.
13
5.
24
5.
69
6.
03
6.
06
6.
24
8.
38
8.
53
9.
37
7.
56
11.
07
12.
47
13.
04
14.
86
13.
23
8.
1
10.
94
9.
85
11.
48
11.
66
10.
14
12.
35
8.
9
7.
98
a
l ti
m
e
v
a
lu
e
o
l
u
e
lin
e- Fo
re
7
08
n
d for
Electri
c
o
r
ne
xt
day
t
o
n
g
th
is year's
d
of im
pl
em
e
n
n
ed & tested
b
f
lin
e im
p
l
e
m
e
o
n
lin
e with t
h
w
ee
n on
line
a
y & real for
e
r
uni
t
of C
h
e
t
.
T
esting Stage
r
e
a
l t
i
m
e data
an
d
a
l T
i
m
e
Resu
l
o
f l
o
adin
g
(k
V
cast
e
d dat
a
o
f
c
Vehicles A
p
p
o
match
with
t
h
real tim
e
da
t
n
t
a
tio
n
with
t
h
b
y
usi
ng B
a
c
k
e
n
t
atio
n
will
c
h
e
real tim
e d
a
& o
f
fline
i
m
ca
s
t
e
d
d
a
ta
f
o
t
an
m
a
in
i, E
V
d
f
o
recasted data
t
by
Tr
aining an
d
T
e
sting
6.
0926
5.
201
5.
6468
5.
944
5.
944
6.
2412
8.
4702
8.
6188
9.
5104
7.
5786
11.
293
6
12.
631
13.
225
4
15.
157
2
13.
522
6
8.
173
11.
145
9.
956
11.
590
11.
888
10.
253
12.
631
8.
916
8.
024
V
)
of
Feb
20
1
6
f
ne
xt
day
)
pp
li
cable ...
.
(
P
t
he
last year'
s
a
ta fo
r in
put
&
t
he m
a
pped d
a
k
pr
op
ag
atio
n
co
nsist o
f
on
d
ata.
m
pl
em
ent
a
ti
o
n
o
r bot
h o
n
l
i
n
e
V
ch
a
r
g
i
ng
s
t
a
d
6
(Red
line-
R
P
o
o
r
ani
S)
2
279
fore
caste
d
&
m
a
pped
a
ta can be
algo
rith
m
e and one
n
and t
h
e
e
& o
f
flin
e
a
tion. Th
e
R
eal time
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEC
E
2
280
F
6.
D
fo
r f
o
th
e p
e
charg
consi
d
E
Vo
l.
6
,
N
o
.
F
ig
ure 4
.
Fin
a
D
IS
CUSSI
O
N
In
th
e lite
r
reca
sting the
e
rform
a
nce o
f
i
n
g dem
a
nd
o
d
ere
d
a fl
eet
o
5, Oct
o
be
r
2
0
Tabl
e 2.
C
S .No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
a
l
forecaste
d
d
N
r
ature survey
d
EV c
h
arging
f
fo
ur
d
i
ffer
e
n
o
f 3,000
EV
o
f 2,
1
30 E
V
a
0
16
:
227
4 –
2
C
om
pari
so
n b
e
Co
m
p
ariso
n
Ti
m
e
00:00:
00
01:00:
00
02:00:
00
03:00:
00
04:00:
00
05:00:
00
06:00:
00
07:00:
00
08:00:
00
09:00:
00
10:00:
00
11:00:
00
12:00:
00
13:00:
00
14:00:
00
15:00:
00
16:00:
00
17:00:
00
18:00:
00
19:00:
00
20:00:
00
21:00:
00
22:00:
00
23:00:
00
d
ata with
real
t
fo
re
c
d
one by
certa
i
d
em
and was
s
n
t d
a
ta m
i
n
i
n
g
was forecas
t
e
n
d
pred
icted
t
22
81
e
tween m
a
p
p
e
n
between
m
a
pp
e
Next Yea
r's
R
Ti
m
e
Da
t
5.
5
5
4.
6
4.
77
5.
5
7.
2
10.
4
12.
4
11.
5
8.
5
8.
8
9
11.
5
15
16
14.
4
11.
5
16.
8
15.
4
12.
4
11
13.
5
11
8
t
ime v
a
lu
e of
c
asted d
a
ta of
i
n
au
tho
r
s it
h
s
t
udi
e
d
. T
w
o
d
g
m
e
t
hods w
a
e
d a
n
d
com
p
a
t
he cha
r
gi
ng
d
e
d
d
a
ta with
t
h
e
d data wi
th this
y
R
eal
t
a
Resul
t
A
n
l
o
ad
i
ng (
k
V
)
next
day
)
h
as bee
n
state
d
d
iffere
nt real
i
a
s evaluated.
I
a
red with th
e
d
em
and of
w
h
h
is year's in
p
u
y
ear's
inpu
t
t
By
Tr
aining
n
d T
e
sting
5.
376
4.
872
4.
368
4.
536
5.
376
7.
224
10.
416
12.
6
11.
592
8.
568
8.
736
9.
072
11.
592
15.
288
16.
296
14.
614
11.
592
17.
136
15.
792
12.
6
11.
088
13.
776
11.
088
8.
064
of
A
u
gust
2
0
1
d
that the use
o
stic stu
d
y
cas
I
n the first st
u
actu
al d
a
ta.
h
ole week
on
a
ISS
N
:
2
ut
1
6
(Red lin
e-
o
f
dat
a
m
i
ni
n
g
s
e where
c
o
ns
i
u
dy case the
T
h
e second
s
a
hal
f
-
h
ou
rl
y
2
088
-87
08
Final
g
m
e
t
hod
s
i
de
re
d an
d
d
ay-ahea
d
s
t
u
dy case
b
as
is. The
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
A N
o
n Li
nea
r
C
ont
r
o
l
l
e
r f
o
r
Forec
a
st
i
n
g t
h
e Ri
si
ng
De
m
a
nd
f
o
r
El
ect
ri
c Vehi
cl
es A
ppl
i
c
abl
e
...
. (
P
o
o
r
ani
S)
2
281
results showed that the data mining m
e
thods can be used
for forecasting the EV c
h
ar
ging load, with increase
d
accuracy es
pec
i
ally when t
h
e
conf
iguration param
e
ters of ea
ch
m
e
thod a
r
e
carefully selected.
Howev
e
r th
e
resu
lts of t
h
e
fin
a
l fo
recastin
g [1
0
]
& Train
i
n
g
& Testing
will b
e
alm
o
st sam
e
, th
ere
won't be
m
u
ch differe
n
ce only the di
ffere
nce will be of data because
that
are using last year's
data for
Training &
Te
sting & m
o
reover t
h
is year'
s
data for
fi
nal forecasting as i
n
put but
the models t
h
at are
being
use
d
for both t
h
e forecasting
will be
sam
e
t
h
at's why the
r
e
won't be m
u
ch
diffe
renc
e in both the
results. The
n
e
w STLF algo
rith
m
will come
with
th
e fo
llo
wing
adv
a
n
c
es.
Th
e m
a
in
p
r
ob
lem
with
th
e
statistica
l
mo
d
e
l wit
h
al
mo
st b
e
so
m
e
ex
p
e
rim
e
n
t
al
erro
r
wh
il
e
im
ple
m
enting the res
u
lt. So to ove
rcom
e this error and gi
ve alm
o
st accurate
ly precise res
u
lt we work wi
th the
neural
network which will de
al with t
h
e
rea
l
tim
e
da
ta
& give
the desire
d result with
l
e
ss
am
ount of error
co
m
p
ared
with th
e
o
t
h
e
r statistical tech
n
i
ques. Neural
n
e
t
w
ork is
p
a
rticu
l
arly ef
fective
in
h
a
nd
ling
o
u
tliers,
al
t
hou
g
h
ot
he
r
m
e
t
hods
al
so c
a
n m
a
ke i
t
do
n
e
at
som
e
ext
e
nt
. T
h
e i
n
p
u
t
t
h
at
i
s
bei
n
g sel
ect
ed by
t
h
e m
a
ppi
n
g
process
before
the final
forec
a
sting, si
nce for t
h
e fi
nal
forecasting the
re
al tim
e
data is
not a
v
ailable
of t
h
e
fi
nal
f
o
reca
st
ed y
ear t
h
at
'
s
w
h
y
we
nee
d
t
o
acqui
re i
t
by
m
a
ppi
n
g
p
r
oce
ss t
h
at
i
s
hi
t
&
t
r
ai
l
m
e
t
hod t
h
at
wi
l
l
be base
d o
n
p
r
evi
ous e
xpe
ri
ences [
4
]
.
Te
m
p
erat
ure & hum
i
d
i
t
y
m
odel
i
ng i
s
bei
n
g do
ne f
o
r t
h
e m
a
ppi
n
g
pr
ocess t
o
get
t
h
e l
o
a
d
i
n
g
dat
a
fo
r t
h
e
next
y
ear so
t
h
at
w
e
can
use t
h
e
d
a
t
a
fo
r t
h
e
m
a
tchi
n
g
pr
ocess
fo
r t
h
e
real forecaste
d data. Tem
p
era
t
ure a
nd
hum
i
dity will give
the m
a
pped l
o
a
d
ing data t
h
at will be use
d
for t
h
e
com
p
arison
wi
th the foreca
sted data
of
t
h
is year so t
h
at we
can enc
o
unte
r
the error
of the
final forecaste
d dat
a
by
seei
n
g
t
h
e
m
a
pped
dat
a
(
m
apped
dat
a
:
app
r
oxi
m
a
t
e
of
t
h
e
real
t
i
m
e
dat
a
).
REFERE
NC
ES
[1]
D.
Park,
et al.
,
“
E
lectr
i
c Lo
ad F
o
recas
ting Us
i
ng an Artifici
a
l
N
eural Network
,
”
IEEE T
r
ans. on Power Syste
m
s,
vol/issue:
6(2), p
p
. 442-449
. 199
1.
[2]
Z. Xinbo
and
C. Jinsai, “Short-term
power s
y
stem lo
ad for
e
casting b
a
sed
o
n
improved BP artif
icial neur
al
network,”
Comp
uter Science an
d Automation
Engineeri
ng (
C
SAE)
, 2011 IEEE Internationa
l Conferen
ce o
n
,
Shanghai, pp. 14
-17, 2011
.
[3]
J. M.
Espinoza,
et a
l
.
, “
E
l
ectr
i
c
Load F
o
re
cas
t
i
n
g
,”
I
E
EE Contro
l Systems
, vol/issue: 27(5)
, pp
. 43
-57, 2007
.
[4]
S.
Ve
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