Electricity Load Forecasting Science and Practices F. Elakrmi 1,*, N. Abu Shikhah 1

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1 Elecriciy od Forecsing Science nd Prcices F. Elrmi,*,. Abu Shihh Fculy of Engineering,Ammn Universiy,Ammn, Jordn * Corresonding uhor. Tel: , Fx: , E-mil: frmi@mmnu.edu.jo Absrc: Elecriciy demnd forecsing lys cenrl role in he rocess of ower sysem lnning nd oerion. This oic hs been, nd is sill rcing vs reserch civiies h re erformed by reserchers in he cdemi nd ower comnies. This is ribued o he fc h beer forecsing of ower imlies reching exc lns wih no over- or under lnning. I should be emhsized h he number of mehods used in demnd forecsing is remendous, nd he selecion of he mos suible forecsing lgorihm is no n esy rocess. Mny fcors nd rmeers mus be considered in he rocess of lod forecsing including he ime frme of he forecs, he licion nd urose of he forecs, weher fcors, culurl nd socil fcors, in ddiion o sysem-secific reled fcors, ll of which will ffec he forecsing rocess. This er discusses he frme wor of his oic, where vrious numbers of mehodologies nd models develoed re demonsred. A descriion of forecsing models hels in idenifying he chrcerisics, feures, nd srenghs of ech model. Keywords: od forecsing echniques, lnning, lod model, ime frme, forecsing ccurcy.. Inroducion Forecsing hs evolved over he yers ino n exc science nd mny models nd ools re resenly vilble commercilly. The min urose of forecsing is o mee fuure requiremens, reduce unexeced cos nd rovide oenil inu o decision ming []. Energy secor received gre enion from counries nd individuls s i leds o comforble life. Wih he dven of incresed civilizion nd economic develomen energy hs become life-susining commodiy. Due o he fc h convenionl energy resources on erh re limied eole sred o loo for new energy resources; esecilly environmenlly benign nd renewble ones. Menwhile, reserchers focused on develoing beer mehodologies for redicing he fuure demnd for energy o mee fuure suly, which will hel counries o ln heir develomen civiies correcly, hus, voiding under-or over-lnning of fuure suly. Elecriciy consiues mjor shre of he ol energy requiremens of mny socieies. Moreover, elecriciy newors lend hemselves o be uilized s sources of live or on-line informion bou elecriciy consumion. On he oher hnd, oering ower sysem hs he mission of mching demnd for elecric energy wih vilble suly, while meeing he execed e demnd of he ower sysem. As such, elecricl demnd forecsing rovides inu o he lnning of fuure resources, where he focus is on ol nnul consumion of elecric energy which is ey fcor in redicing sysem requiremens. Forecsing is brodly clssified in he lierure, in he conex of ime frmes, s: ) long-erm forecsing (-0 yers), b) medium-erm (- monhs), nd c) shor-erm (-4 wees hed), nd d) very shor erm (-7dys hed) [, 3]. ong-erm lod forecsing is inended for licions in cciy exnsion, nd long-erm cil invesmen reurn sudies. Medium-erm forecsing is uilized in rering minennce scheduling, nd o ln for ouges nd mjor wors in he

2 ower sysem. Shor-erm forecsing is used in oerion lnning, uni commimen, nd economic disching. The very-shor erm forecsing is devoed for lod exchnge nd conrcing wih neighboring newors, nd o minin secure ower sysem. Becuse elecricl energy cnno be sored roriely, ccure lod forecsing is very imorn for he correc invesmens. I cn be confidenly sed h he "science" of elecriciy lod forecsing hs reched n dvnced level. This field rcs he enion of he indusry nd cdemi, nd is erformed higher levels of ower comnies nd cdemic reserch. However, furher collborion beween he cdemic nd indusril fields is mus which shll imminenly led o beer imlemenion of his science in rel world nd shll resul in more roseriy o he socieies in erms of beer uilizion of he scrce resources of our lne [4]. I mus be emhsized h rior o he selecion of forecsing model cerin fcors mus be sudied nd ssessed in order o gurnee selecing suible model. These fcors include he following:. Se of he economy b. Cler vision of lnning c. Tye of economy d. Sus of he elecric ower sysem e. Sus of elecriciy mre f. Undersnding of he inerrelions wih oher energy forms g. Inegring oher demnd mniulion rogrms in he forecsing The mim objecive of his er is o give quic, however, exclusive overview of he science nd rcices of lod forecsing. The er is orgnized s follows: secion discusses he ime frmes involved in lod forecsing, he vrious mehods of lod forecsing re resened in secion 3. The erformnce of he vilble mehods re illusred in secion 4, nd secion 5 resens he conclusions of he er.. Elecricl Forecsing Time frmes In ower uiliies, he generl rcice gives he resonsibiliy for conducing he Shor-erm lod forecsing (STF), nd Medium-erm lod forecsing (MTF) o sysem oerion dermens (e.g. Generion, Trnsmission, nd Disribuion oerions). On he oher hnd, ong-erm lod forecsing (TF) is ssigned for he lnning dermen. However, oher dermens use he esimed forecss for conducing vrious sudies reled o finncil nd invesmen lnning wihin he uiliy. These cegories re briefly discussed in he following... Shor-erm nd Very Shor-erm Forecsing STF focuses on redicing elecricl hourly lods nd energy demnd for eriods u o one wee hed ing ino ccoun h lod demnd is highly volile on dy o dy bsis. STF is very crucil elemen in he rocess of ower sysem oerionl lnning h ffecs he erformnce of mny funcions. Such funcions cover lod flow sudies, securiy nd coningency nlysis, economic disch, uni commimen, hydro-herml coordinion, revenive minennce ln for he generors, rnscion evluion, relibiliy evluion of he ower sysem nd rding of ower in inerconneced sysems.

3 Severl fcors ffec STF including: ) rend effecs, ) sesonl effecs, 3) secil effecs, 4) weher effecs, 5) rndom effecs such s: humn civiies, lod mngemen, ricing sregy, nd elecriciy riff srucures. Moreover, sudden chnges in sysem demnd or sysem ouges reresen noher ye of unceriny ssocied wih lod forecsing rocess. All of he bove dds o he comlexiy of geing n ccure STF for elecricl lods, nd ress o focus on he differen fcors involved in his rocess nd in he coninuous develomen of new mehodologies o minimize he errors encounered... Medium-erm Forecsing MTF is suible for ower comnies for minennce lnning. The forecs eriod is from severl wees o monhs hed. This ye of forecs deends minly on growh fcors, i.e. fcors h influence demnd such s min evens, ddiion of new lods, demnd erns of lrge fciliies, nd minennce requiremens of lrge consumers. This ye of forecs is no concerned wih hourly lods lie shor erm forecs, bu rher redics he e lod of dys or for he wees hed. Wih his informion i cn be decided o wheher e cerin fciliies/lns for minennce or no during given eriod of ime. The mehods used for his ye of forecs re similr o he shor erm forecs exce h here is less need for ccurcy [5]..3. ong-erm Forecsing As he nme imlies TF is used o ln he exnsion of he ower sysem, i.e. wh ye of generion or rnsmission ln(s) re needed, when, where, nd wh size. Usully generion sysem lnning is done serely from rnsmission sysem lnning [5]. The sudy eriod of his forecs is from yer o 5-0 yers hed. The ouu of his forecs is usully he e lod nd nnul energy requiremen of he sysem. Th is o sy h he e lod nd energy requiremen for he coming yers of he sudy eriod re deermined by he forecsing mehod. Usully economeric or regression nlysis mehods re widely used in his ye of forecs. However, end-use nd exer sysem mehods re lso used [6]. 3. Forecsing Mehods The forecsing mehods re generlly clssified ino: ) sisicl-bsed mehods, nd ) rificil inelligence-bsed mehods. There is no cler reference of one grou of mehods over he oher. I ll deends on he licion on hnd. However, due o dvens in comuer echnology in he hrdwre nd sofwre res, he rificil inelligence-bsed mehods hve recenly overen he sisicl-bsed mehods nd re being doed by more users he resen ime. A shor discussion of he differen scoes nd echniques nd models reresening hese mehods re resened in he following. 3. Sisicl-bsed mehods Sisicl-bsed mehods re widely used in mny brnches of forecsing. For elecriciy demnd forecsing, hese mehods run well under norml condiions, however, heir erformnce worsens during bru chnges in environmenl or 3

4 4 sociologicl vribles h ffec lod erns. Moreover, hose echniques require lrge number of comlex relionshis, ccomnied by long comuionl imes, nd my resul in numericl insbiliies. These mehods include: Regression mehods In regression mehods, he lod d is ssumed o fi re-defined funcion or model hs unnown rmeers. The regression mehod is used o find ou he oimum se of hese unnown rmeers, h mes he nown d nd he forecsed d resul in he minimum sum of squred errors. Mny models exis including: A- iner Here, he model is described s: () ˆ 0 Where, ˆ : is he h esimed lod bsed on he seleced model : is he ime of he lod (cn be hour, dy, ec) 0, : re he model unnowns o be esimed : is he index of d =,,, The unnowns re found using: ) ( 0 B- Polynomil The model is described s: ) ( ˆ m m m 3 0 The unnown rmeers re esimed using: ) ( 4 0 C- Seleced-model funcion The model funcion cn be chosen o be ny resonble funcion e.g. exonenil, logrihmic, ec, nd he oimizion is done bsed on minimizing he sum of squred errors beween originl nd rediced lods.

5 D- Muli-vrible The model is ssumed o be given y: ˆ b b X b X 0 5 Where, : is he h esimed lod bsed on he seleced model : re he indeenden vribles, i=,,, nd he b's re ermed he "regression coefficiens" o be esimed. ˆ X i 3.. Time series mehods Time series mehods re bsed on he ssumion h he d hve n inernl srucure, such s uocorrelion, rend, or sesonl vriion. Time series forecsing mehods deec nd exlore such srucure [5, 7]. The objecive is o ssess he bes model nd hence exrole fuure forecss. The srucurl comonens of he ime series model cn be inegred o led o he forecs. Two iner-relionshis cn be formed s shown in he following equions: ˆ T c S I (8) or, ˆ T c Where, he comonens noions re : lod, =ime index, T=rend, C=cyclic, S=sesonl, I =irregulr, nd he h indices he forecs. Differen models re imlemened, ll of which see o filer ou, sere, he ssumed comonens. Some of hese mehods re discussed below. A- ARMA (uoregressive moving verge) which is used ssuming sionry rocesses. Oher vriions include ARIMA (he cronym of uoregressive inegred moving verge, lso nown s Box-Jenins model), ARMAX, nd ARIMAX (uoregressive inegred moving verge wih exogenous vribles), nd FARMAX (fuzzy uoregressive moving verge wih exogenous inu vribles) re used ssuming non-sionry rocesses. The mhemicl formulion of hese models is well formuled nd is vilble in he lierure [4, 8]. B- Exonenil Smoohing is used when he vrible o be rediced is no sble. This smoohing will filer ou such vriions o ge he underlying rend. A simle smoohing formul is given s: P S b I X ( ) (9) i ˆ ( ) i ( 0 ) i Where, : is he h smoohed lod. : is smoohing fcor wih 0<<. ˆ C- The Princil comonen Anlysis (PCA) PCA ims o sere he bsic srucure or ern of he lod from he disurbnce or rndom comonen (filering rocess more or less). In oher words PCA is used for reducing he dimension of mulivrie d ses, where vribles re highly correled, o smller se of vribles. This in urn, reduces he 5

6 number of vribles ffecing he lod nd leds o beer forecs. The min drwbc of he PCA is h i requires long comuionl ime, nd he difficuly of selecing he oimum order of he rincil comonens [9, 0]. D- Similr-dy roch Hisoricl lods re serched o find he lods wih similr chrcerisics wihin one, wo, or hree yers o erform he forecs dy. Similr chrcerisics include weher, dy of he wee, nd he de. The lod of similr dy is considered s forecs. iner combinion or regression rocedure h cn include severl similr dys cn be imlemened. Moreover, he rend coefficiens cn be used for similr dys in he revious yers []. Oher mehods h lie in his cegory include he Economeric or cusl mehod, nd he Simulion or End-Use Mehods 3.3. Arificil Inelligence (AI) - bsed mehods The mjoriy of he AI-bsed echniques focus on STF which is necessry for oerion lnning. The rionle behind i being h he rndomness inroduced o lods in STF is smll nd he redicions will be more ccure. In conrs, TF hs lrge degree of unceriny due o he lrger ime frme h mes he AI-bsed mehodologies less efficien nd resul in lrge forecsing errors when comred o rdiionl mehods. Differen AI-bsed echniques re discussed in he following. A- eurl newors. The rificil neurl newors (A or simly ) mehods hve been widely used s n elecric lod forecsing echnique in Shor-erm lod forecsing (STF) since 990. A mehods re usully lied o erform non-liner curve fiing. The lierure hs vriey of A ublicions in he ower sysem od forecsing [-5]. Figure () shows yicl bloc digrm of A scheme. Figure () A wih Bc Progion rchiecure I should be noed h for lod forecsing roblem, he inu vecor (which feeds he inu lyer) my include differen rmeers ffecing he lod such s emerure, 6

7 humidiy, nd revious hourly, dily, monhly, nd yerly lods, ec. The ouu vecor for he cse of lod forecsing cn be he esimed lods he required ime level. The inu vribles cn be clssified ino he following clsses: hisoricl lods, hisoricl nd fuure emerures, hour of dy index, dy of wee index, wind-seed, sy-cover, rinfll, nd we or dry dy. For norml lod redicion, A ouerforms convenionl mehods. However, A res bnorml d (e.g. sudden chnge of lod) s bd-redings, which re yiclly negleced. Some reserch hs been erformed o imrove he A erformnce in such cses by incororing rnsien deecor h is uilized o increse he ccurcy of lod redicion in rnsien se. The combinion of muli-resoluion echniques (he wvele rnsform) in conjuncion wih A resuled in decresing redicion error in STF he exense of exr comuionl ime [6]. B- Exer sysems The use of exer sysems-bsed echniques begn in he 960 s, nd hey wor bes when humn exer is vilble o wor wih sofwre develoers for considerble moun of ime in imring he exer s nowledge o he exer sysem sofwre. Also, n exer s nowledge mus be rorie for codificion ino sofwre rules (i.e. he exer mus be ble o exlin his/her decision rocess o rogrmmers). An exer sysem my codify u o hundreds or housnds of roducion rules [7, 8]. In he forecsing field, hisoricl oeror s nowledge nd he hourly observions, weher rmeers, nd ny imorn fcors reled o forecsing mus be incorored nd shred beween he ries conribuing o he building u of he exer sysem. In generl erms he develoed lgorihms erform beer comred o he convenionl sisicl mehods. The more incororion of he cul exerience of sysem oerors differen sies will serve in imroving he erformnce of he forecs. C- Fuzzy logic sysems In he sense of lod forecsing, fuzzy logic does no need recise models reling inus nd ouus nd disurbnce. The roer selecion of rules nd reled logic of his mehod becomes robus when used for forecsing [7]. Once he fuzzy inus re logiclly rocessed, n inverse rocess clled he "defuzzificion" cn be used o roduce he ouus. Fuzzy logic sysems cn be lied for STF s well s for TF. For exmle: n AFIS (Adive ewor bsed on Fuzzy Inference Sysem) ws used for TF nd i showed more ccure demnd forecsing using minimum economeric or end-user informion [4]. Anoher exmle resens he DMS (Disribuion Mngemen Sysems) model which ws used successfully o redic lods boh subsion nd feeder levels [9,0]. D- Suor vecor mchines (SVM) SVMs nd is les squres version [, ] reresen more recen nd owerful lerning echnique h is used for solving d clssificion nd regression roblems. Boh mehods reresen lerning SVM h erform nonliner ming of he d ino high dimension (referred o s ming he ernel funcions o feures). 7

8 SVCMs use simle liner funcions o cree liner decision boundries in he new sce. The min roblem is h of choosing suible ernel for he SVMs [-3]. The mehod ws lied o STF nd roduces comeiive resuls comred o h of he sisicl mehods. E- The Pricle Swrm Oimizion (PSO) lgorihm The PSO is new dive lgorihm bsed on socil-sychologicl mehor h my be used o find oiml (or ner oiml) soluions o numericl nd quliive roblems. Mos ricle swrms re bsed on wo socio-meric rinciles. The rincile is bsed on he fc h ricles fly hrough he soluion sce, nd re influenced by boh he bes ricle (clled globl bes) in he ricle oulion nd he bes soluion h curren ricle hs discovered so fr. The bes osiion h hs been visied by he curren ricle is doned by (locl bes). The (globl bes) individul conceully connecs ll members of he oulion o one noher. The ricle swrm oimizion mes use of velociy vecor o ude he curren osiion of ech ricle in he swrm. The osiion of ech ricle is uded bsed on he socil behvior h oulion of individuls ds o is environmen by reurning o romising regions h were reviously discovered [4]. 4. Performnce 5. Conclusions References. Mongomery, D.C., Johnson,.A., & Grdiner, J.S. (990), Forecsing & Time Series Anlysis, ew Yor: McGrw-Hill.. Solimn, S.A., Almmri, R.A., El-Hwry, M.E., & Temrz, H.K (004), ong- Term Elecric Pe od Forecsing For Power Sysem Plnning: A Comrive Sudy, The Arbin Journl for Science nd Engineering, 9(B), Srivsv, S.C., & Venrmn, D. (997), Shor-erm od Forecsing Using Recurren eurl ewors, APSCOM-97, vol., (45-50). Hong Kong. 4. F. Elrmi, nd. Abu-Shihh, cher -"Elecriciy Demnd Forecsing" 5. Elrmi, F., & Abu Shihh,. (007, Seember), Power Sysem Anlysis nd Sudies: Cooerion beween Acdemi nd Elecriciy Comnies. Per resened CIGRE Conference, Ammn, Jordn. 6. Khled, M., E-ggr, K.M. & A-Rumih K.A. (005), Elecric od Forecsing Using Geneic Bsed Algorihm, Oiml Filer Esimor nd es Error Squres Technique: Comrive Sudy, Proceedings Of World Acdemy Of Science, Engineering And Technology (PWASET) 6, (. 38-4). 7. Bowermn, B.., O'Connell, R.T., & Koehler A.B. (005), Forecsing, Time Series, nd Regression: An Alied Aroch, Belmon, Cliforni: Thomson Broos/Cole. 8

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