Different Methods of Long-Term Electric Load Demand Forecasting; A Comprehensive Review

Transcription

1 Dfferent Methods of Long-Term Electrc Load Demand Forecastng; A Comprehensve Revew L. Ghods* and M. Kalantar* Abstract: Long-term demand forecastng presents the frst step n plannng and developng future generaton, transmsson and dstrbuton facltes. One of the prmary tasks of an electrc utlty accurately predcts load demand requrements at all tmes, especally for long-term. Based on the outcome of such forecasts, utltes coordnate ther resources to meet the forecasted demand usng a least-cost plan. In general, resource plannng s performed subject to numerous uncertantes. Expert opnon ndcates that a major source of uncertanty n plannng for future capacty resource needs and operaton of exstng generaton resources s the forecasted load demand. Ths paper presents an overvew of the past and current practce n long- term demand forecastng. It ntroduces methods, whch conssts of some tradtonal methods, neural networks, genetc algorthms, fuzzy rules, support vector machnes, wavelet networks and expert systems. Keywords: Long-term, Demand Forecastng, Neural Networks, Genetc Algorthms, Fuzzy Rules. 1 Introducton1 A power system serves one functon and that s to supply customers, both large and small, wth electrcal energy as economcally and as relablty as possble. Another responsblty of power utltes s to recognze the needs of ther customers (Demand) and supply the necessary energes. Lmtatons of energy resources n addton to envronmental factors, requres the electrc energy to be used more effcently and more effcent power plants and transmsson lnes to be constructed [1]. Long-term demand forecasts span from eght years ahead up to ffteen years. They have an mportant role n the context of generaton, transmsson and dstrbuton network plannng n a power system. The objectve of power system plannng s to determne an economcal expanson of the equpment and facltes to meet the customers' future electrc demand wth an acceptable level of relablty and power qualty [2]. Accurate long-term demand forecastng plays an essental role foe electrc power system plannng. It corresponds to load demand forecastng wth lead tmes enough to plan for long-term mantenance, constructon schedulng for developng new generaton facltes, purchasng of generatng unts, developng transmsson and dstrbuton systems. The accuracy of the long-term Iranan Journal of Electrcal & Electronc Engneerng, Paper frst receved 18 Jan and n revsed form 13 Jun * The Authors are wth the Department of Electrcal Engneerng, Iran Unversty of scence and technology (IUST), Tehran, Iran. E-mals: Ladan_gh760@yahoo.com, Kalantar@ust.ac.r. load forecast has sgnfcant effect on developng future generaton and dstrbuton plannng. An expensve overestmaton of load demand wll result n substantal nvestment for the constructon of excess power facltes, whle underestmaton wll result n customer dscontentment. Unfortunately, t s dffcult to forecast load demand accurately over a plannng perod of several years. Ths fact s due to the uncertan nature of the forecastng process. There are a large number of nfluental that characterze and drectly or ndrectly affect the underlyng forecastng process; all of them uncertan and uncontrollable [3]. However, nether the accurate amount of needed power nor the preparaton for such amounts of power s as easy as t looks, because: (1) long-term load forecastng s always naccurate (2) peak demand s very much dependant on temperature (3) some of the necessary data for longterm forecastng ncludng weather condton and economc data are not avalable, (4) t s very dffcult to store electrc power wth the present technology, (5) t takes several years and requres a great amount of nvestment to construct new power generaton statons and transmsson facltes [4]. Therefore, any long-term load demand forecastng, by nature, s naccurate! Generally, long-term load demand forecastng methods can be classfed n to two broad categores: parametrc methods and artfcal ntellgence based methods. The artfcal ntellgence methods are further classfed n to neural networks [1], [2], [4], [8], [10], support vector machnes [15], genetc algorthms [14], Iranan Journal of Electrcal & Electronc Engneerng, Vol. 7, No. 4, Dec

2 wavelet networks [12] [13], fuzzy logcs [16] and expert system [17] methods. The parametrc methods are based on relatng load demand to ts affectng factors by a mathematcal model. The model parameters are estmated usng statstcal technques on hstorcal data of load and t's affectng factors. Parametrc load forecastng methods can be generally categorzed under three approaches: regresson methods, tme seres predcton methods [3]. Tradtonal statstcal load demand forecastng technques or parametrc methods have been used n practce for a long tme. These tradtonal methods can be combned usng weghted mult-model forecastng technques, showng adequate results n practcal system. However, these methods cannot properly present the complex nonlnear relatonshps that exst between the load and a seres of factors that nfluence on t [2]. In ths paper, we ntroduce a bref overvew n longterm forecastng methods. Ths paper s organzed as follows. Next secton brefly descrbes parametrc models. Secton III descrbes dfferent artfcal ntellgence based methods and secton IV s the conclusons of paper. 2 Parametrc Methods The three types of well-known parametrc methods are as, trend analyss, end-use modelng and econometrc modelng. 2.1 Trend Analyss Trend analyss extends past rates of electrcty demand n to the future, usng technques that range from hand-drawn straght lnes to complex computerproduced curves. These extensons consttute the forecast. Trend analyss focuses on past changes or movements n electrcty demand and uses them to predct future changes n electrcty demand. Usually, there s not much explanaton of why demand acts as t does, n the past or n the future. Trendng s frequently modfed by nformed judgment, wheren utlty forecasters modfy ther forecasts based on ther knowledge of future developments whch mght make future electrcty demand behave dfferently than t has n the past [5]. The advantage of trend analyss s that, t s smple, quck and nexpensve to perform [5]. The dsadvantage of a trend forecast s that t produces only one result, future electrcty demand. It does not help analyze why electrcty demand behaves the way t does, and t provdes no means to accurately measure how changes n energy prces or government poltes nfluence electrcty demand [5]. 2.2 End-Use Models The end-use approach drectly estmates energy consumpton by usng extensve nformaton on end users, such as applcatons, the customer use, ther age, szes of houses, and so on. Statstcal nformaton about customers along wth dynamcs of change s the bass for the forecast [5]. End-use models focus on the varous uses of electrcty n the resdental, commercal, and ndustral sector. These models are based on the prncple that electrcty demand s derved from customer's demand for lght, coolng, heatng, refrgeraton, etc. Thus, enduse models explan energy demand as a functon of the number of applcatons n the market [5]. Ideally, ths approach s very accurate. However, t s senstve to the amount and qualty of end-use data. For example, n ths method the dstrbuton of equpment age s mportant for partcular types of applances. End-use forecast requres less hstorcal data but more nformaton about customers and ther equpments [5]. Ths method predcts the energy consumptons. If we want to calculate the load, we have to have the load factor n each sectons and dfferent types of energy consumptons and then by load factor we can calculate the load n each secton. The system load factor s defned as follows equaton: Average - load demand LoadFactor = Peak - load demand (1) annual KWh energy = peak - load demand 8760 hours/year The dsadvantage of end-use analyss s that most end-use models assume a constant relatonshp between electrcty and end-use (electrcty per applance). Ths mght hold for over a few years, but over 10 or 20-year perod, energy savng technology or energy prces wll undoubtedly change, and the relatonshps wll not reman constant [6]. 2.3 Econometrc Models The econometrc approach combnes economc theory and statstcal technques for forecastng electrcty demand. The approach estmates the relatonshp between energy consumpton (dependent varables) and factors nfluencng consumpton. The relatonshps are estmated by the least-square method or tme seres methods. One of the optons n ths framework s to aggregate the econometrc approach, when consumpton n dfferent sectors (resdental, commercal, ndustral, etc.) s calculated as a functon of weather, economc and other varables, and then estmates are assembled usng recent hstorcal data. Integraton of the econometrc approach n to the enduse approach ntroduces behavoral components n to the end-use equatons [5]. The advantage of econometrcs are that t provdes detaled nformaton on future levels of electrcty demand, why future electrcty demand ncreases, and how electrcty demand s affected by all the varous factors [6], [7], [29]. 250 Iranan Journal of Electrcal & Electronc Engneerng, Vol. 7, No. 4, Dec. 2011

3 A dsadvantage of econometrc forecastng s that n order for an econometrc forecast to be accurate, the changes n electrcty reman the same n the forecast perod as n the past. Ths assumpton, whch s called constant elastcty, may be hard to justfy especally where very large electrcty prces changes, make customers more senstve to electrcty prces. 2.4 Dfferences Between These Tradtonal Mentoned Method As mentoned n the trend analyss, just past changes or movements n electrcty demand and uses them to predct future changes n electrcty demand, there sn't any process on why those movements happened. In ths method, end users and ther behavor aren't mportant. But n end use method, statstcal nformaton about customers along wth dynamcs of change s the bass for the forecast. In Economcal methods, the results estmate the relatonshp between dependent varables and factors nfluencng consumpton. The relatonshps are estmated by the least-square method or tme seres methods. In comparson, trend analyss can't be trustworthy; n ths method we need a wse and knowng judge to recognze unreal date and omt them from prevous nformaton. Up to ths part we descrbe the old methods for long term load forecastng. They are also useful today. But the new followng methods can use for ther accuracy and fast possessng system. Specal the new methods are used for dfferent economcal nputs n forecastng. By these new methods, we can have a model from the past data and correct ts naccurate date. After that we can predct the followng peak load. 3 Artfcal Intellgence Based Methods 3.1 Artfcal Neural Networks Artfcal neural networks (ANNs) have succeeded n several power system problems, such as plannng, control, analyss, protecton, desgn, load forecastng, securty analyss, and fault dagnoss. The last three are the most popular. The ANNs ablty n mappng complex non-lnear relatonshps s responsble for the growng number of ts applcaton to load forecastng [8], [9]. Most of the ANNs have been appled to shorttme load forecastng. Only a few studes are carres out for long-term load demand forecastng [10], [22], [24], [28]. In developng a long-term load forecast, the followng are some of the degrees of freedom whch must be terated upon wth the objectve to ncrease the potental for an accurate load forecast: (1) fracton of the database that wll be used for tranng and testng purpose, (2) transformatons to be performed on the hstorcal database, (3) ANNs archtecture specfcatons, (4) optmal stoppng pont durng ANNs tranng, and, (5) relatve weghts for use n forecast combnaton [8]. The desgn of neural network archtecture nvolves decson makng on type, sze and number of neural beng used [11]. The result of Output ANN s n (2). Y n = = 1 W X (2) where = 1, 2,..., n, X s nput, and W s weght of network and Y s one of the ANN's outputs. The frst queston to be asked s f an ANN can learn to perform the desre applcaton, and f so what would be the most sutable form of the network. In ths secton, varous aspects of ANNs are analyzed to determne a sutable model. These aspects nclude the network archtecture and method of tranng. There are three types whch can be useful for long-term load demand forecastng: Recurrent neural network (RNN) for forecastng the peak load, feed-forward back propagaton (FFBP) for forecastng the annual peak load [10] and radal bass functon network.(rbfn) for fastng tranng and better followng the peaks and valleys [4]. 1) Recurrent neural network: Recurrent neural network contans feedback connectons, whch enable them to encode temporal context nternally. Ths feedback can be external or nternal. RNN has be ablty to learn patterns from the past records and also to generalze and project the future load patterns for an unseen data [10]. We have dfferent types of RNNs, such as Jordan RNN, Elman RNN and others. Feedback connectons n these RNNs are dfferent from network to network. For nstance, Jordan RNN has feedback connectons from output to nput whle the Elman RNN has feedback connectons from ts hdden layer neurons back to ts nputs. Addtonal neurons n nput layer, whch accept these feedback connectons, are called state or context neurons. The role of context neurons n RNN s to get nputs from the upper layer, and after processng send ther outputs to the hdden layer together wth other plan unts. In long-term load demand forecastng, there s strong relatonshp between the present and next year loads. For ths type of problem, Jordan's model of RNN proved to be sutable. However, t should be noted that as the perod of target forecast loads becomes longer, the forecast errors mght ncreases relatvely [10]. Ths s why the feed-forward back propagaton s used for forecastng loads of longer than 1 year. The Jordan RNN used n most of case study s shown n Fg. 1. 2) Feed-forward back propagaton: Feed-forward back propagaton s one of s one of the most wdely used neural network paradgms, whch have been appled successfully n applcaton studes. FFBP can be appled to any problem that requres pattern mappng. Gven an nput pattern, the network produces an assocated output pattern. Its learnng and update procedure s ntutvely appealng, because t s based on Ghods & Kalantar: Dfferent Methods of Long-Term Electrc Load Demand Forecastng 251

4 a relatvely smple concept: the network s suppled wth both a set of patterns to be learned and desred system response for each pattern. If the network gves the wrong answer, then the weghts are corrected so that the error s lessened and as a result future responses of the network are more lkely to be correct. The advantages of usng such a network center on some of ther propertes, too. Frstly, they automatcally generalze ther knowledge enablng them to recognzee patterns, whch they have had seen. Secondly, they are robust enough to recognze patterns, whch have been obscured by nose. Lastly, once they have been traned on the ntal set of patterns, ther recognton of smlar patterns s accomplshed very quckly [10]. There are two more advantages for FFBP, BP tranng s mathematcally desgned to mnmze the mean square error acrosss all tranng patterns and t has supervsed tranng technque [10]. The FFBP used n most of case study s shown n Fg. 2. 3) Redal bass functon network: A redal bass functon network (RBFN) n most general terms s any network, whch has an nternal representaton of hdden processng elements (pattern unts), whch are radcally symmetrc [ 4]. It conssts of three layers; the nput layer, hdden layer and output layer. The nodes wthn each layer are fully connected to the prevous layer, as shown n Fg. 3. For a hdden unt to be radcally symmetrc, t must have the followng three consttuents: A center, whch s a vector n the nput space and whch s typcally stored n the weght vector from the nput layer to the pattern unt A dstance measure, to determne how far an nput vector s from the center. Typcally, ths s the standard Eucldean dstance measure. A transfer functon, whch s a functon of a sngle varable, and whch determnes the output of the processng elements by mappng the outpu of the dstance functon. A common functon s a Gaussan functon, whch outputs stronger value when the dstance s small. In the other word, the output of a pattern unt s a functon of only the dstance between an nput vector and the stored center. Fg. 1 Jordan Recurrent Neural Networks. Fg. 2 Feed-forward back propagaton neural networks. Fg. 3 Redal Bass Functon Network. The key to a successful mplementaton of thesee networks s to fnd sutable centers for the Gaussan functon [10]. 3.2 Wavelet Networks Ths secton nvestgates the applcaton of wavelet packet n power load forecastng. Wavelet theory s ntroduced to power load forecastng recently and receved wde attenton. Comparng to tradtonal load forecastng methods, wavelet theory provdes powerful and flexble tool to decompose load data nto dfferent frequency components, makng t possble to analyze the characterstcs of each component and mprove forecastng accuracy. Wavelet packet analyss s the extenson of wavelet analyss and t has better frequency resoluton [12], [23]. Several papers nvestgated the applcaton of wavelet analyss n power load forecastng, and the forecastng accuracy was mproved. The wavelet network must have the followng measurements to forecast well: Select proper wavelet functon for load forecastng. The selecton of wavelet functon s mportant for wavelet applcaton, but there s no general rule tll now.generally, the most mportant thng of power load forecastng s to 252 Iranan Journal of Electrcal & Electronc Engneerng, Vol. 7, No. 4, Dec. 2011

5 mprove forecastng accuracy, so t requres less dstorton durng the wavelet decomposton and reconstructon process, whch s dfferent from other applcatons such as fault detecton. Among varous wavelet functons, the borghogonal wavelet functonn s symmetrcal and has lnear phase, so t won t brng sgnal dstorton durng the decomposton and reconstructon process. Therefore, the selecton of borghogonal wavelet for power load forecastng s sutable [12]. Avod border dstorton durng wavelet transform. Because of the lmted number of data n wavelet applcatons, border dstorton problem arsess durng the wavelet decomposton process, but users often gnore t except the method proposed n [13]. For load forecastng applcaton wth fnte number of load data, border dstorton means the wavelet coeffcents of the latest data s dstorted and the forecastng based on these coeffcents couldn t gve accurate forecastng result. It s too bad to forecast wthout the help of the latest data. To deal wth the problem, border extenson s a smple and effectve soluton, and there are several types of border extenson, such as symmetrcal extenson and zero paddng. Although t s hard to gve general gudelnes for selectng proper extenson method, symmetrcal border extensonn s sutable for borghogonal wavelet whch s symmetrcal. The structure of the wavelet network s shown n Fg. 4. Ths structure s very smlar to Mult layer neural network. The most advantage factor of wavelet network s not spanned nputs although the accuracy of model s better than mult layer neural networks. It has more advantages to apply to long-term forecast. The mult- has resoluton analyss capablty of wavelet functons much power n functon approxmaton to obtan better accuracy [13]. A new method s the fuzzy rules and wavelet neural network model for long term load forecastng. The neural call functon s bass of nonlnear wavelets. It overcomes the shortcomng of sngle tran set of fuzzy rules. It can mprove effectvely the forecast accuracy and speed [26]. Another new method s the wavelet neural network model for long term load forecastng. The neural call functon s bass of nonlnear wavelets. We overcome the shortcomng of sngle tran set of ANN. It can be seen that ths method can mprove effectvely the forecast accuracy and speed [30]. 3.3 Genetc Algorthms Managngg electrcal energy supply s a complex task. The most mportant part of electrc utlty resource plannng s forecastng of the future load demand n the regonal or natonal servce area. Ths s usually acheved by constructng models on relatvee nformaton, such as clmate and prevous load demandd data. Genetc programmng approach s proposed to forecast long term electrcal power consumpton. The emprcal results demonstrate successful load forecast wth a low error rate [19]. Genetc Algorthms (GAs) have recently receved much attenton as robust stochastc search algorthms for varous problems. Ths class of methods s based on the mechansm of natural selecton and natural genetcs, whch combnes the noton of survval of the fttest, random and yet structured, search and parallell evaluaton of the ponts n the search space. GAs have been successfully appled n varous areas such as, load flow problems, fault detecton, stablty analyss, economc dspatch, power system control and load demand forecastng [14]. Consderng the features of long term load forecastng are complcated, a generc neural network model that s able to adapt to and learn from amount of non-lnear or mprecse rules, so t s a model wth hghly robustness. For avodng the nflexblty of the generc neural network tself, many experences and opnons of experts are ntroduced durng the use, so that a comprehensve effect of dfferent factors thatt nfluence the power load can be reflected. The generc algorthm s able to search precsely at global scope, and the neural network s able to ft well at local scope, both of whch are chosen together [20]. Genetc algorthms are a numercal optmzaton technque. More specfcally, they are parameter search procedures based upon the mechancs of natural genetcs. They combne a Darwnan survval-of-the-fttestt strategy wth a random, yet structured nformatonn exchange among a populaton of artfcal chromosomes. Ths technque has ganed popularty n recent years as a robust optmzaton tool for a varety of problems n engneerng, scence, economcs, fnance, etc. GAs accommodate all the facets of soft computng, namely uncertanty, mprecson, non-lnearty, and robustness. Some of the attractve features can be summarzed as followng [14]: Fg. 4 Schematc of Wavelet Network. Ghods & Kalantar: Dfferent Methods of Long-Term Electrc Load Demand Forecastng 253

6 Learnng: GAs are the best known and wdely used global search technques wth an ablty to explore and explot a gven operatng space usng avalable performance (or learnng) measures. Generc Code Structure: GA operates on an encoded parameter strng and not drectly on the parameters. Ths enables the user to treat any aspect of the problem as an optmzable varable. Optmalty of the Solutons: In many problems, there s no guarantee of smoothness. Tradtonal search technques often fal mserably on such search spaces. GA s known to be capable of fndng near optmal solutons n complex search spaces. Advanced Operators: Ths ncludes technques such as nckng (for dscoverng multple solutons), combnatons of Neural, Fuzzy, and chaos theory, and multple-objectve optmzaton. The GAs approach presented n ths work s employed to fnd the optmum values of the state vector X that mnmzes the absolute summaton of the forecastng error r(t). In order to emphasze the best strng and speed up convergence of the teraton procedure, ftness s normalzed nto range between 0 and 1. The ftness functon (ff) adopted s [14]: 1 ff = 1 k m (3) + k = 1 r(t) where k s a scalng constant (for example, k=0.0001) Lke other stochastc methods, the GA has a number of parameters that must be selected, sze of populaton, probablty of crossover and probablty of mutaton. r (t) s the error vector assocated. GA tres to keep the r (t) n the allowed lmtaton. If the r (t) s kept n the allowed lmtaton, the ftness functon has the best value for load demand forecastng [14]. Forecastng results usng GA were found to be the best. Ths ndcates that the GA approaches s qute promsng and deserves serous attenton because of ts robustness and sutablty for parallel mplementaton [14]. Wth r (t), we can calculate the load demand forecastng by the followng equaton: n P(t) = a + = a t + r(t) (4) 0 1 where P(t) s the peak load demand at tme t, a 0, a are the regresson coeffcents relatng the load demand P(t) to the tme t. r(t) s the resdual load at year (t). 3.4 Support Vector Machne (SVM) SVM (Support Vector Machne) s a useful technque for data classfcaton. Even though people consder that t s easer to use than Neural Networks, however, users who are not famlar wth SVM often get unsatsfactory results at frst [15]. The support vector machnes (SVMs) are based on the prncple of structural rsk mnmzaton (SRM) rather than the prncple of emprcal rsk mnmzaton, whch conducted by most of tradtonal neural network models. SVMs have been extended to solve nonlnear regresson estmaton problems [16]. Recurrent neural network (RNN) s one knd of SVM whch s based on the man concept n whch every unt s consdered as an output of the network and the provson of adjusted nformaton as nput n a tranng process. RNNs are extensvely appled n long-term load tme seres forecastng and can be classfed n three types, Jordan networks, Elman networks, and Wllams and Zpser networks. Both Jordan and Elman networks use manly past nformaton to capture detaled nformaton. Wllams and Zpser networks take much more nformaton from the hdden layer and back nto themselves. Therefore, Wllams and Zpser networks are senstve when models are mplemented. Jordan and Elman networks are suted to tme seres forecastng. Tradtonally, RNNs are traned by back-propagaton algorthms. SVMs wth genetc algorthms are used to determne the weghts between nodes [16]. The basc concept of the SVM regresson s to map nonlnearly the orgnal data x nto a hgher dmensonal feature space. Hence, gven a set of data N G = {(x,a )} = 1 (where x s the nput vector, a the actual value and N s the total number of data patterns), the SVM regresson functon s: f = g(x) = w (x) ψ + b (5) where ψ (x) s the feature of nputs, and both w and b are coeffcents. The coeffcents ( w and b) are estmated by mnmzng the followng regularzed rsk functon: 1 N 1 2 r(c) = C ( a, f ) N 1 Γ ε = + 2 ω (6) where, 0 f a f ε Γε (a, f ) = (7) a f ε otherwse and C and ε are prescrbed parameters. In (6), Γε (a, f) s called the ε-nsenstve loss functon. The loss equals zero f the forecasted value s wthn the ε-tube (7). The second term, 1/2 w ², measures the flatness of the functon. Therefore, C s consdered to specfy the tradeoff between the emprcal rsk and the model flatness. Both C and ε are user-determned parameters. The archtecture of SVMs wth genetc algorthm (SVMG) s shown n Fg. 5. The superor performance of the RSVMG model has several causes. Frst, the RSVMG model has nonlnear 254 Iranan Journal of Electrcal & Electronc Engneerng, Vol. 7, No. 4, Dec. 2011

7 Fg. 6 Block dagram of the fuzzy logc system. Prevous data Load data Fg. 5 Archtecture of SVMG. Fuzzfcaton Selecton of ANN mappng capabltes and thus can more easly capture electrcty load data patterns than can the ANN and regresson models. Second, mproper determnng of these three parameters wll cause ether over-fttng or under-fttng of a SVM model. In ths secton, the Gas can determne sutable parameters to forecast electrcty load. Thrd, the RSVMG model performs structural rsk mnmzaton rather than mnmzng the tranng errors. Mnmzng the upper bound on the generalzaton error mproves the generalzaton performance compared to the ANN and regresson models. 3.5 Fuzzy Logc Model Fuzzy control systems are rule-based systems n whch a set of so-called fzzy rules represents a control decson mechansm to adjust the effects of certan stmulus. The am of fuzzy control systems s normally to replace a sklled human operator wth a fuzzy rulebased system. The fuzzy logc model provdes an algorthm, whch can convert the lngustc strategy based on expert knowledge nto an automatc strategy. Fg. 6 represents the basc confguraton of a fuzzy logc system, whch conssts of a fuzzfcaton, knowledge base, fuzzy nterface and a defuzzfcaton (IO). The fuzzy logc method s appled for scorng. The applcaton of fuzzy rules wll mprove the model accuracy by avodng arbtrarness for the purpose of the stud. The fuzzy rule base s composed of some rules generated from the analyss of the hstorcal load data [16], [21]. One of the applcatons of the fuzzy rules s to combne them wth neural network to tran ANN and have a better load demand forecastng. The tranng patterns for the ANN models were collected from the hstorcal load data. The number of tranng cycles has been determned through a tral process, to avod overtranng [16]. The beneft of the proposed hybrd structure was to utlze the advantages of both,.e., the generalzaton capablty of ANN and the ablty of fuzzy nference for handlng and formalzng the experence and knowledge of the forecasters. It has been demonstrated that the method gve relatvely accurate load forecasts for the actual data. The test results showed that ths method of Fuzzy Interface Defuzzfcaton Load forecasted Fg. 7 Structure of ANN and Fuzzy based used. Forecasted of selected load Combnaton of the results Forecasted load forecastng could provde a consderable mprovement of the forecastng accuracy. Ths ndcates that the fuzzy rules and the tranng patterns for the ANN s qute promsng and deserve serous attenton of ts robustness and sutablty for mplementaton. It can be concluded that the outcome of the study clearly ndcates that the proposed composte model can be used as an attractve and effectve means for the ndustral load forecastng. The mprovement of forecast accuracy and the adaptaton to the change of customers would fulfll the forecastng needs [16]. Fg. 7 shows the structure of ANN and Fuzzy based used n forecastng. We can also combne two dfferent methods to acheve better result. These two methods can be ANN and Fuzzy control. Long term load forecastng of power system s affected by varous uncertan factors. Usng clusterng method numerous relatve factors can be syntheszed for the forecastng model so that the accuracy of the load forecastng would be mproved sgnfcantly. A clusterng neural network consstng of logc operators s quoted, whch can be used n long term load forecastng Applyng logc operators and n the fuzzy theory, the algorthm speed of the clusterng network wll be ncreased. Although compettve learnng algorthm s used here for the network, t solves the dead unt problem and gves more room to select the ntal values of the clusterng center n the clusterng analyss of the hstory data. The proposed model consders the nfluences of both hstory and future uncertan factors [25], [27], [31], [32]. Ghods & Kalantar: Dfferent Methods of Long-Term Electrc Load Demand Forecastng 255

8 3.6 Expert System The confdence levels assocated wth classcal forecastng technques, when appled to forecastng problem n mature and stable utltes are unlkely to be smlar to those of dynamc and fast growng utltes. Ths s attrbuted to the dfferences n the nature of growth, soco-economc condtons, occurrence of specal events, extreme clmatc condtons, and the competton n generaton due to the deregulaton of the electrcty sector wth possble changes n tarff structures. Under such condtons, these forecastng technques are nsuffcent to establsh demand forecast for long-term load demand. Consequently, ths case requres separate consderaton ether by pursung the search for more mprovement n the exstng forecastng technques or establshng another approach to address the forecastng problem of such systems [17]. In ths secton, the classcal forecastng methods are frstly appled to obtan the long-term load demand forecasts, for a typcal fast growng utlty as well as normal developng system [17]. A poor performance s observed when such methods are appled to fast developng system, whereas most of these models are vald when used to produce the forecasts of the normal developng system. Consequently, an extended logstc model s developed to reflect the crtcal forecastng problem n fast growng areas. Although the developed model gves an accurate load demand forecast compared wth the classcal models, t s hardly dffcult to dependent on sngle method for producng the demand of such fast growng and dynamc system [17]. Ths s because several mportant factors related to the cyclc and dynamc events that contrbute sgnfcantly to the system load are dffcult to nvolve t nto the exstng forecastng models. Thus, there s a need to develop a computatonal tool whch allows one to store the knowledge assocated wth ths problem along wth the mathematcal models to support the choce of the most sutable load forecastng model, for long-term power system plannng. Therefore, the mplementaton of long-term forecastng strateges usng a knowledge-based expert system (ES) s then presented n ths secton. In the expert system, key system varables whch have major effects on system load are dentfed based on past planners experences. A set of decson rules relatng these varables are then establshed and stored n the knowledge base to select the recommended forecastng [17]. The man components of the proposed expert system are shown n Fg. 8. Wth the knowledge base at hand (rules and facts), an nference engne can be used to search through ths knowledge base accordng to the soluton strategy. The detaled procedures of the soluton strategy to ascertan the accuracy and credblty of selectng forecastng method. In addton to knowledge base, nference engne, soluton strategy, a user nterface s also developed n the expert system to facltate the navgaton between the expert system and the user. The varables of the formulated problem can be grouped nto Statc and Dynamc Facts as follows: Statc Facts: Ths knd of knowledge s developed before startng the plannng process. A sample of these facts s: system condtons to dentfy the current stuaton of the system,.e., mature, or under developng, solated or nterconnected wth other system, etc. Forecastng horzon to defne the load forecastng perod,.e., long-term. Load pattern to descrbe the load behavor (stable, or dynamc pattern, cyclc, or seasonal pattern, or combnaton of all, tme of system peak, load types, etc.) Hstorcal peak load to ndcate annual and seasonal growth, concdence factor, area peak, etc. Hstorcal energy to descrbe the nformaton related to number of consumers of each sector, consumpton rate, tarff rate, etc. Major factors affectng the system peak,.e., weather varables, economc varables, demographc varables, specal event, suppressed demand, bulk loads to be connected nto the network, concdence factor of the system peak, etc. Dynamc Facts: These facts are developed and automatcally updated durng the nference process to represent the plannng attrbutes needed for evaluatng a decson makng process. Samples of these facts nclude the followng: Fg. 8 Structure of expert system for long-term load forecastng. 256 Iranan Journal of Electrcal & Electronc Engneerng, Vol. 7, No. 4, Dec. 2011

9 load and energy attrbute for the estmated load and energy forecast; System losses attrbute for the estmated system losses; Error attrbute related to the forecastng model. In ths secton, a long-term load forecastng s developed and classfed accordng to the forecastng problem usng a knowledge-based expert system (ES). The proposed methodology s appled successfully to forecast yearly peak load for normal and fast developng power systems. Snce the expert system s very flexble n updatng the forecastng methods and heurstc rules, t s expected that the expert system can serve as a valuable assstant to system planners n performng ther annual load forecastng dutes. Fnally, t can be expected to serve as a valuable assstant also for tranng purposes [17]. 4 Contrastng New Forecastng Methods Recurrent neural network (RNN) has be ablty to learn patterns from the past records and also to generalze and project the future load patterns for an unseen data. In ths type, some addtonal neurons are avalable. Addtonal neurons n nput layer, whch accept these feedback connectons, are called state or context neurons. The role of context neurons n RNN s to get nputs from the upper layer, and after processng send ther outputs to the hdden layer together wth other plan unts. In the other neural network method, feed-forward back propagaton an nput pattern s gven, the network produces an assocated output pattern. Its learnng and update procedure s ntutvely appealng, because t s based on a relatvely smple concept: the network s suppled wth both a set of patterns to be learned and desred system response for each pattern. Ths method s much better than the RNN method. Because f the network gves the wrong answer, then the weghts are corrected so that the error s lessened and as a result future responses of the network are more lkely to be correct. It can have a trustworthy result. Another method for long term load forecastng s Wavelet Network. The most advantage factor of wavelet network s not spanned nputs although the accuracy of model s better than mult layer neural networks. Ths s one reason whch can be ended up n ths choce. It has more advantages to apply to long-term forecast. The mult-resoluton analyss capablty of wavelet functons has much power n functon approxmaton to obtan better accuracy. Ths accuracy can make a better result n future forecastng. For one method, we can call Genetc Algorthm for long-term load demand forecastng, Genetc algorthms are a numercal optmzaton technque. More specfcally, they are parameter search procedures based upon the mechancs of natural genetcs. Forecastng results usng GA were found to be the best. Ths ndcates that the GA approaches s qute promsng and deserves serous attenton because of ts robustness and sutablty for parallel mplementaton. Other most commonly method s SVM. Ths method s much more comparable wth ANN. Frst, the RSVMG model has nonlnear mappng capabltes and thus can more easly capture electrcty load data patterns than can the ANN and regresson models. Second, mproper determnng of these three parameters wll cause ether over-fttng or under-fttng of a SVM model. Thrd, the RSVMG model performs structural rsk mnmzaton rather than mnmzng the tranng errors. Fuzzy system as another method s normally to replace a sklled human operator wth a fuzzy rule-based system. One of the applcatons of the fuzzy rules s to combne them wth neural network to tran ANN and have a better load demand forecastng. In expert system, we can use tradtonal methods to forecast the peak load forecastng. The expert system s very flexble n updatng the forecastng methods and heurstc rules, t s expected that the expert system can serve as a valuable assstant to system planners n performng ther annual load forecastng dutes. 5 Conclusons Load forecastng plays a domnant part n the economc optmzaton and secure operaton of electrc power systems. Long-term load forecastng represents the frst step n developng future generaton, transmsson, and dstrbuton facltes. Any substantal devaton n the forecast, partcularly under the new market structure, wll result n ether overbuldng of supply facltes, or curtalment of customer demand. The confdence levels assocated wth classcal forecastng technques, when appled to forecastng problem n mature and stable utltes are unlkely to be smlar to those of dynamc and fast growng utltes. Ths s attrbuted to the dfferences n the nature of growth, soco-economc condtons, occurrence of specal events, extreme clmatc condtons, and the competton n generaton due to the deregulaton of the electrcty sector wth possble changes n tarff structures. Under such condtons, these forecastng technques are nsuffcent to establsh demand forecast for long-term power system plannng. Consequently, ths case requres separate consderaton ether by pursung the search for more mprovement n the exstng forecastng technques or establshng another approach to address the forecastng problem of such systems. Dfferent methods of long-term load demand forecastng are defned n ths paper. All of these methods can forecast the load of the power system, but the amount of prevous data and such varables whch they need to forecast, make them dfferent n accuracy from area to area. Fnally, for long-term load forecastng, we should know the power system n detals, and after that we can select the best method for the specfed power system. Ghods & Kalantar: Dfferent Methods of Long-Term Electrc Load Demand Forecastng 257

10 Sometmes we can combne dfferent methods and compare the accuracy of them together. Tradtonal methods, such as tme seres, regresson models and etc. are used n most of the countres, because of ther relable result. Neural networks can solve nonlnear problems, and because of nonlnear behavor of load, so they can be useful for long-term load forecastng. Genetc algorthm can forecast long-term load forecastng, when we have a lot amount of dfferent varables and we want to fnd the best soluton to follow the future load. Also t can be useful to estmate the support vector machne parameters. Wavelet can estmate peak and valley of load behavor better than Furous seres. It can combne wth ANN have a better forecast. References [1] Al Mamun M., and Negasaka K., Artfcal neural networks appled to long-term electrcty demand forecastng, Proceedngs of the Fourth Internatonal Conference on Hybrd Intellgent Systems (HIS'04), pp , Dec [2] Dang Khoa T. Q. and Oanh P. T., Applcaton of Elman and neural wavelet network to long-term load forecastng, ISEE Journal, track 3, sec. B, No. 20, pp. 1-6, [3] Al-Hamd H. M. and Solman S. A., Longterm/md-term electrc load forecastng based on short-term correlaton and annual growth, Electrc Power System Research (Elsever), Vol. 74, No. 3, pp , June [4] Negasaka K. and Al Mamun M., Long-term peak demand predcton of 9 Japanese power utltes usng radal bass functon networks, IEEE Power Eengneerng Socety General Meetng, Vol. 1, pp , 6-10 June [5] Engneerng and desgn hydropower proponent, Load forecastng methods, n EM , appendx B, Dec [6] Genethlou D. and Fenberg E. A., Load forecastng, Appled mathematcs for restructured electrc power system: optmzaton, control and computatonal ntellgence (J. H. Chow, F.F. Wu, and J. J. Momoh, eds.), chapter 12, pp , [7] Fu C. W. and Nguyen T. T., Models for longterm energy forecastng, IEEE Power Engneerng Socety General Meetng, Vol. 1, pp , July [8] Taradar Heque M. and Kashtban A. M., Applcaton of neural networks n power systems; A revew, Transacton of Engneerng, Computng and Technology, Vol. 6, No. 1, ISSN , pp , June [9] Atya A. F., Development of an ntellgent longterm electrc load forecastng system, Proceedngs of the Internatonal Conference, ISAP apos, pp , [10] Kermanshah B. S., and Iwamya H., Up to year 2020 load forecastng usng neural nets, Electrc Power System Research (Elsever), Vol. 24, No. 9, pp , [11] Phmphachanh S., Chamnongtha K., Kumhom P., and Sangswang A., Usng neural network for long term peak load forecastng n Ventane muncpalty, IEEE Regon 10 Conference, TENCON 2004, Vol. 3, pp , [12] Khoa T. Q. D., Phuong L. M., Bnh P. T. T. and Len N. T. H., Applcaton of wavelet and neural network to long-term load forecastng, Internatonal Conference on Power System Technology (POWERCON 2004), pp , Sngapore, November [13] Khoa T. Q. D., Phuong L. M., Bnh P. T. T., Len N. T. H., Power load forecastng algorthm based on wavelet packet analyss, Internatonal Conference on Power System Technology (POWERCON 2004), pp , Sngapore, November [14] EL_Naggar K. M. and AL-Rumah K. A., Electrc load forecastng usng genetc based algorthm, optmal flter estmator and least error square technque: Comparatve study, Transacton of Engneerng, Computng and Technology, Vol. 6, pp , ISSN , June [15] Pa P.-F., and Hong W.-C., Forecastng regonal electrcty load based on recurrent support vector machnes wth genetc algorthms, Electrc Power System Research (Elsever), Vol. 74, No. 3, pp , [16] Faraht M. A., Long-term ndustral load forecastng and plannng usng neural networks technque and fuzzy nterface method, 39th Internatonal Unverstes Power Engneerng Conference, UPEC 2004, Vol. 1, pp , [17] Kandl M. S., El-Debeky S. M. and Hasanen N. E., The mplementaton of long-term forecastng strateges usng a knowledge-based expert system: part-ii, Electrc Power System Research (Elsever), Vol. 58, No. 1, pp , [18] Carmona D., Jaramllo M. A., Gonzalez E. and Alvarez A. J., Electrc energy demand forecastng wth neural networks, IEEE, 28th Annual Conference of the Industral Electroncs Socety, Vol.3, pp , [19] Karabuluta K., Alkanb A. and Ylmaz A. S., Long term energy consumpton forecastng genetc programmng, Assocaton for Scentfc Research, Mathematcal And Computatonal Applcatons, Vol. 13. No. 2, pp , [20] Ynglng, Hongsong S., Yawe Y. and Nansheng D., Research on long term load forecastng 258 Iranan Journal of Electrcal & Electronc Engneerng, Vol. 7, No. 4, Dec. 2011

11 based on Improved Genetc Neural Network, IEEE, PCAIIA, pp , [21] Zhang Q. and Lu T., Research on the md-long term electrc load forecastng based on fuzzy rules, Informaton Management and Engneerng (ICIME), 2010 The 2nd IEEE Internatonal Conference, pp , [22] Ghanbar A., Naghav A., Ghader S. F. and Sabaghan M., Artfcal Neural Networks and regresson approaches comparson for forecastng Iran's annual electrcty load, IEEE POWER ENG. Conference, pp , [23] J Z., Zhang P. and Zhao Z., Applcaton of Wavelet Neutral Network and Rough Set Theory to Forecast Md-Long-Term Electrc Power Load, Educaton Technology and Computer Scence, ETCS '09. Frst Internatonal Workshop on, Vol. 1, pp , [24] Hobbs N. J., Km B. H. and Leee K. Y., Long- Neural Network Archtecture, Intellgent Systems Applcatons to Power Systems, ISAP Internatonal Conference on Dgtal Object Identfer, pp. 1-7, Term Load Forecastng Usng System Type [25] Yue L., Zhang Y., Xe H. and Zhong Q., The fuzzy logc clusterng neural network approach for mddle and long term load forecastng, GSIS IEEE Internatonal Conference, pp , [26] Zhang Q. and Lu T., A Fuzzy Rules and Wavelet Neural Network Method for Md-long- term Electrc Load Forecastng, ICCNT, IEEE 2010 Second Internatonal Conference, pp , [27] Ghanbaran M., Kavehna F., Askar M. R., Mohammad A. and Kevan H., Applyng Tme- Neuro-Fuzzy Technques, IEEE Conference POWER ENG., pp , [28] Shrvastava V. and Msra R. B., A Novel Approach of Input Varable Selecton for ANN Seres Regresson to Load Forecastng Usng Based Load Forecastng, IEEE Conference, ICPST, pp. 1-5, [29] Yngyng L. and Dongxao N., Applcaton of Prncpal Component Regresson Analyss n power load forecastng for medum and long term, IEEE Conference, ICACTE, pp. V V3-203, [30] Zhang Q. and Lu T.., Research on Md-long Term Load Forecastng Base on Wavelet Neural Network, IEEE Conference, ICCEA 2010, pp, , [31] Maralloo M. N., Koushk A. R., Lucas C. and Kalhor A.., Long term electrcal load forecastng va a neurofuzzy model, IEEE Conference, CSICC 2009, pp 35-40, [32] Dalvand M. M., Azam S. and Tarmorad H., Long-term load forecastng of Iranan power grd usng fuzzy and artfcal neural networks, IEEE Conference 2008, pp 1-4, [33] Ghods L. and Kalantar M., Long-Term Peak Load Demand Forecastng by Usng Radal Basss Functon Neural Networks, Iranan Journal of Electrcall & Electronc Engneerng (IJEEE), Vol. 6, No. 3, pp , Ladan Ghods was born n Tehran, Iran n She receved her B.S.. degree from Azad Unversty of Tehran. She graduated n power engneerng n M.S from Iran unversty of scence and technology (IUST) n She s nterested n neural network and ts abltes to forecast the future factors. She s also nterested n power nstallaton especally n ndustres. Mohsen Kalantar, was born on 1961 n Iran. He receved hs Ph.D. from Indan Insttute of Technology, New Delh, Inda n He s currently an Assocate Professor n the Departmentt of Electrcal Engneerng at Iran Unversty of Scence and Technology, Tehran. He s also foundng member of Center of Excellence for Power System Automaton and Operaton. He has around 30 Journal publcatons and has presented about 1000 papers at Internatonal Conferences. Hs felds of nterestt nclude wnd and solar power generatons, Dstrbuted Generatons, power system dynamcs and control, system stablty and optmzaton. Ghods & Kalantar: Dfferent Methods of Long-Term Electrc Load Demand Forecastng 259