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ARTICLE REVIEW

ELECTRICAL LOAD FORECASTING METHODS

Submitted by:

Jonatan C! "a#a$doME"%EE

Submitted to:

D&! A$$an So&iano"&o'e((o&ia$ Le#tu&e&


2/20

ISSUES TO ADDRESS:

Main Question:

Wat i( te be(t metod to 'o&e#a(t E$e#t&i#a$ Load)

Specifc Question:

Wat a&e te a*ai$ab$e metod( u(ed in 'o&e#a(tin+ e$e#t&i#a$ $oad)

Paper search history:

Sea Too$: S#ien#e Di&e#t (ea '&om ISI Web

,ea& S-an: ./0/%./01

Keywords No. O Articles

Forecasting 30, 838Load Forecasting 7, 031Electrical Load Forecasting 3, 050Electrical Load Forecasting Methods 2, 804

SELECTED PAPERS FOR REVIEW:

PAPER Title: LONG TERM PEAK LOAD FORECASTING OF KUTAHYA

USING DIFFERENT APPROACHES.Authors: ,$ma2 A($an3 Se&4an ,a*a(#a3 Ce$a$ ,a(a&!u"lication: Inte&nationa$ Jou&na$ on Te#ni#a$ and "y(i#a$ "&ob$em( o'

En+inee&in+3 June ./003 I((ue 0! Vo$! 5 Numbe& .3 -a+e(67%80

PAPER !Title: A WAVELET ELMAN NEURAL NETWORK FOR SHORT-

TERM ELECTRICAL LOAD PREDICTION UNDER THEINFLUENCE OF TEMPERATURE.

Authors: San9ay e$o3 San9ay Dudu$!u"lication: E$e#t&i#a$ "o;e& and Ene&+y Sy(tem(3 15 5%

0/70

PAPER "Title: LONG TERM LOAD FORECASTING FOR THE

EGYPTIAN NETWORK USING ANN AND REGRESSIONMODELS.


3/20

Authors: Wa+dy Man(ou&3 Moamed Moene(3 Ha((an Mamoud3Amed Ga&eeb

!u"lication: .0(t Inte&nationa$ Con'e&en#e on E$e#t&i#ity Di(t&ibution3F&an4'u&t3 >%8 June ./00

Research C!"ar#s$:

S%&'( O)*ec%#+es

!a#er 1 Study te $on+ te&m -ea4 $oad 'o&e#a(tin+ 'o& te #ity o'utaya3 Tu&4ey!

!a#er 2 To -&edi#t 0%day aead e$e#t&i#a$ -o;e& $oad unde& tein?uen#e o' e u(in+ a #ombination o' Wa*e$et and E$mannet;o&4( a( a &e#u&&ent neu&a$ net;o&4!

!a#er 3 Lon+ Te&m $oad 'o&e#a(tin+ 'o& E+y-tian uni@ed net;o&4and #om-a&in+ te &e(u$t +ene&ated by ANN


4/20


5/20

Ti( a--&oa# (o;( a -$ane in te (-a#e ;it t&ee dimen(ion( ;i# #anbe e-&e((ed a( +i*en in euation


6/20

Sin#e te Euation


7/20

#ontinuou( 'un#tion bet;een / and 0 a( e-&e((ed in Euation


8/20

PROPOSE) PRE)'+$'ON $E+(N'Q%E

A ty-i#a$ $oad -&o@$e o' 0 ;ee4 #ontain( ba(i# #om-onent3 -ea4 and *a$$ey#om-onent(3 a*e&a+e #om-onent3 -e&iodi# #om-onent and &andom #om-onent!Con*entiona$ te#niue( $i4e time (e&ie( metod( and &e+&e((ion ba(ed metod(a&e ab$e to et&a#t on$y (ome o' te(e #om-onent( '&om te i(to&i# data! ut3 ANN(by it( non%$inea& natu&e #an et&a#t a$$ te #om-onent( '&om te t&ainin+ data!Ho;e*e&3 EN mode$ed on o&i+ina$ $oad data #ou$d not et&a#t a$$ o' tem in a ;e$$de@ned manne&! A #e&tain &e+u$a&ity o' te data i( an im-o&tant -&e#ondition 'o& te

(u##e(('u$ a--$i#ation o' NN(!

Fi+! 5 (o;( ;o&4in+ b$o#4 dia+&am o' -&o-o(ed -&edi#tion -&o#e((! He&e3idea i( to de#om-o(e te die&ent (ea(ona$ $oad (e&ie( u(in+ mu$ti%&e(o$utionana$y(i(


9/20

aite#tu&e #on(i(t( o' @*e in-ut(! Te numbe& o' "E( in te idden $aye& i( 0/ andtat in te out-ut $aye& i( one!

Opti1al desi2n o El1an RNN as a load predictor or detail co1ponent/)!0 at deco1position le3els o two

Te E$man RNN i( mode$ed on te i+ '&euen#y #om-onent D. in o&de& to-&edi#t it 'o& 0%day%aead $oad demand -&edi#tion in (umme& (ea(on! Te in-ut and

out-ut *a&iab$e( a&e no&ma$i2ed in te &an+e o' /0! Te &e(u$t( o' *a&iation o'numbe& o' in-ut( a&e +&a-ed in Fi+! ! It( #a&e'u$ in(-e#tion &e*ea$( tat te*a&iou( -e&'o&man#e mea(u&e( (u# a( MSE3 NMSE3 rand MA"E a&e noti#ed at tei&minima$ #o&&e(-ondin+ to numbe& o' in-ut( (et to 5! Hen#e3 ti( aite#tu&e#on(i(t( o' t&ee in-ut(!

Opti1al desi2n o El1an RNN as a load predictor or detail co1ponent/)0 at sin2le deco1position le3el

Te in-ut and out-ut *a&iab$e( o' E$man RNN a&e no&ma$i2ed in te &an+e o'/0! Te numbe& o' in-ut *a&iab$e(3 t&an('e& 'un#tion in out-ut $aye& and $ea&nin+

-a&adi+m 'o& ti( ty-e o' -&edi#to& i( o-timi2ed 'o& minimum e&&o& on -&edi#tiondata (et! Te &e(u$t( o' *a&iation o' numbe& o' in-ut( a&e +&a-ed in Fi+! 7! It(#a&e'u$ in(-e#tion &e*ea$( tat te *a&iou( -e&'o&man#e mea(u&e( (u# a( MSE3NMSE3 & and MA"E a&e noti#ed at tei& minima$ #o&&e(-ondin+ to te o-tima$ *a$ueo' in-ut( (et to 5! Hen#e3 ti( aite#tu&e #on(i(t( o' t&ee in-ut(! Te numbe& o'"E( in te idden $aye& i( 0/ and tat in te out-ut $aye& i( one!


10/20

Th#s "a"er &se' 4a+e,e% !&,%#-res,&%#$ a$a,(s#s %he ,a' ser#esare 'ec!"se' % '#ere$% s&)-ser#es sh4#$ '#ere$% 3re8&e$c(charac%er#s%#cs 3 %he ,a'. E,!a$ $e%4r2 ;EN< #s "%#!a,,( 'es#$e' a$'%ra#$e' &s#$ s%a%#c )ac2 "r"aa%#$ a,r#%h! )ase' $ %he "%#!#=a%#$3 "er3r!a$ce !eas&res s&ch as !ea$ s8&are errr crre,a%#$ce>c#e$% a$' !ea$ a)s,&%e "erce$%ae errr $ %es% "re'#c%#$'a%ase%. Feas#)#,#%( 3 Da&)ech#es 4a+e,e% a% '#ere$% sca,es 4#%h s%a),e

$&!)er 3 'ec!"s#%#$ ,e+e,s #s #$+es%#a%e' % chse %he )es% r'er3r '#ere$% seas$a, ,a' ser#es. The es%#!a%e' !'e,s are e+a,&a%e'+er '#ere$% %e!"era%&re a$' h&!#'#%( #$ r'er % e/a!#$e %he#r #!"ac%$ acc&ra%e ,a' "re'#c%#$.

PAPER "

AR$'-'+'A# NE%RA# NE$ORK

In ti( (e#tion ;e -&o*ide a b&ie' int&odu#tion to! A&ti@#ia$ Neu&a$ Net;o&4(a( been moti*ated &i+t '&om it( in#e-tion '&om te ;ay tat te uman b&ain-&o#e((e( in'o&mation! In +ene&a$ neu&a$ net;o&4( a&e (im-$y matemati#a$

te#niue( de(i+ned to a##om-$i( a *a&iety o' ta(4(! ANN i( #om-o(ed o' ba(i##om-utin+ -&o#e((in+ e$ement(


11/20

#onne#tion3 and a #on(tant bia( te&m3 &e-&e(ented in te @+u&e by te ;ei+t o' a#onne#tion ;it a @ed in-ut eua$ to 0! Te a#ti*ation 'un#tion mu(t be a non%de#&ea(in+ and die&entiab$e 'un#tion te mo(t #ommon #oi#e( a&e te (i+moida$


12/20

Ti( euation ;i$$ be a--$ied 'o& a$$ data #ontained in data (et3 and $oad 'o&ea# yea& ;i$$ be #a$#u$ated! Fi+u&e 1 (o;( 'o&e#a(ted &e(u$t( '&om te -&o-o(edmode$ and te a#tua$ $oad(!

Here %he a&%hrs are c$cer$e' 4#%h ,$ %er! ,a' 3recas%#$ a$'"rese$%s a c!"ar#s$ )e%4ee$ %4 !'e,s a"",#e' % %he E("%#a$&$#?e' $e%4r2 %hese !'e,s are Ar%#?c#a, Ne&ra, Ne%4r2 ;ANN< !'e,a$' reress#$ !'e,. Da%a "re-"rcess#$ %ech$#8&es ha+e )ee$ a"",#e'% #!"r+e 3recas%#$ acc&rac( 3 %he !'e,. Frecas%#$ ca"a)#,#%( 3each a""rach #s e+a,&a%e' )( ca,c&,a%#$ %4 se"ara%e s%a%#s%#ca,e+a,&a%#$s 3 %he Mea$ A)s,&%e Perce$%ae Errr ;MAPE< a$' %he

A+erae A)s,&%e Perce$%ae Errr ;AAVE


13/20

I% ca$ )e see$ 3r! %he res&,%s %ha% %he res&,%s a%%a#$e' 4#%h %he,#$ear a$' e/"$e$%#a, reress#$ a""rach are +er( c,se % each %her.

Fo& e-onentia$ and (im-$e $inea& &e+&e((ion a--&oa#e( te i+e(t-&edi#tion e&&o&( a&e 'ound in O#tobe& ;it a 'o&e#a(tin+ e&&o& o' %6!58 and %6!6 in Janua&y &e(-e#ti*e$y! A( 'o& te ANN te i+e(t 'o&e#a(tin+ e&&o& i( in De#embeo&&e(-ondin+ to%8!6 ! F&om te(e &e(u$t( it i( e*ident tat ;it te $o;e(t mean

ab(o$ute -eenta+e e&&o&


14/20

Te 'ea(ibi$ity o' Daube#ie( ;a*e$et o' o&de&( . a( been in*e(ti+ated and&e(u$t( a*e #$ea&$y 9u(ti@ed te #oi#e o' Db1! It a( been 'ound out tat Db1 i(#on(i(tent$y -e&'o&min+ &ea(onab$y in a$$ (ea(on(! Ho;e*e&3 a &e(o$ution $e*e$ u- to. i( adeuate 'o& a$$ (ea(on(!

'N-#%EN+E O- $EMPERA$%RE ON S$#P

It a( been ob(e&*ed '&om te


15/20

PAPER "

0! Fo& ANN mode$ u(in+ GD" and -o-u$ation ;e +et a##u&ate 'o&e#a(t( ;it

MA"E /!.0/1 ;it *e&y (im-$e t;o $aye& (t&u#tu&e!

.! y #an+in+ te t&ainin+ (et to u(e te ne; 'a#to& &e-&e(ented inGD"Q"O"3 ;e +et mo&e &edu#ed e&&o& o' /!06!5! y a--$yin+ ti( metodo$o+y to 'o&e#a(t bot ene&+y (a$e( and -ea4 $oad'o& yea& .//7!We +et 'o&e#a(t( ;it MA"E o' /!.61!1! T;o ! Ti( mode$ +i*e( &e$ati*e$y +ood &e(u$t(3 i' #om-a&ed to ote&&e+&e((ion mode$( -&o-o(ed in $ite&atu&e! Te 'o&e#a(ted $oad ;a( 07657!6MW3 ;it -eenta+e e&&o& 5!0/83 te a*e&a+e e&&o& 'o& .1 ob(e&*ation(

eua$( 5!.76 !


16/20


17/20

SIGNIFICANT FINDINGS

PAPER

In ti( -a-e&3 u-on #om-a&i(on o' te 'o&e#a(tin+ 'o& .//6 &e(u$t( attained3 it;a( 'ound out tat ;it te u(e o' $on+e& in-ut data3 te 'o&e#a(tin+ e&&o& i(de#&ea(ed! In ti( (tudy3 ;en te die&ent $oad 'o&e#a(tin+ te#niue( a&e#om-a&ed 'o& utaya3 it i( (een tat te ANN a--&oa# a( -&odu#ed bette& &e(u$t(!

PAPER !


18/20

Te ma9o& #ont&ibution o' ti( -a-e& i( te in#&ea(e in te 0%dayaead-&edi#tion a##u&a#y in a$$ (ea(on( by em-$oyin+ te -&o-o(ed no*e$ #ombination o';a*e$et%E$man net;o&4! Te -&o-o(ed te#niue oe&( &e$iab$e and en#ou&a+in+&e(u$t(! Te em-i&i#a$ &e(u$t( (o; tat3 Db1 i( #on(i(tent$y -e&'o&min+ &ea(onab$yin a$$ (ea(on(! Re(o$ution u- to Le*e$ . i( adeuate 'o& (umme&3 &ainy and ;inte&(ea(on(!

It a( been ob(e&*ed tat te DMA"E and maimum A"E a&e (ma$$ 'o&tem-e&atu&e a( a (in+$e ;eate& 'a#to&! Fu&te&3 $one -&edi#ted umidity a( ;e$$ a(-&edi#ted bot tem-e&atu&e and umidity ;e&e a$(o #on(ide&ed in o&de& to +au2ete ee#t on -&edi#tion -e&'o&man#e!

PAPER "

In ti( -a-e&3 te &e(u$t o' te ne; metodo$o+y o' #ombinin+ one o& t;o*a&iab$e( to +et a ne; t&ainin+ *a&iab$e &edu#e( (i+ni@#ant$y te 'o&e#a(tin+ e&&o&!A$(o3 -&e%-&o#e((in+ o' t&ainin+ data (et a( a noti#eab$e ee#t in im-&o*in+'o&e#a(tin+ a##u&a#y!

+ON+#%S'ON

$his article re3iew shows that a1on2 the 1ethods a3aila5le or

electric power load orecastin26 Artifcial Neural Networ7 /ANN0 5ased

1ethods 2i3es the 5est result. 't si2nifcantly reduces the orecastin2

error.

$his re3iew also shows that co15inin2 two or 1ore 3aria5les 2i3es a

1uch 5etter result in orecastin2 electrical load.

$his also shows that when the a3aila5le data has a lon2er ti1e span6

the result o orecastin2 would 5e 1ore accurate.

+OMMEN$S

&i3en that the area co3ered 5y the ollowin2 research was 3ery

lar2e6 still6 the authors deli3er a 2ood result and su22ests a 1uch 5etter

1odel in electric power load orecastin2 which is 3ery helpul in anotherstudy and i such 1ethod will 5e applied in a di8erent area.

)ata processin2 done 5y the researchers usin2 Mat#a5 sotware is

ad3anta2eous 5ecause o its 5uilt9in co1putin2 unctions that can 5e

applied in load orecastin2 with ease and such reduces the ris7 o

co1putin2 errors.


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*y 2i3in2 helpul su22estions on the di8erent 1ethods to 5e applied

in load orecastin26 the researchers contri5uted to solutions o predictin2

accurate 3alues or uture loads which in turn lessens the 5urden o doin2

a 1ore relia5le ener2y plannin2.


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