IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 17, NO. 2, MAY 2002 491

Long-Term Load Forecasting for Fast DevelopingUtility Using a Knowledge-Based Expert System

M. S. Kandil, S. M. El-Debeiky, Senior Member, IEEE, and N. E. Hasanien

AbstractThe application of the classical forecasting methods,when applied to fast developing utility with a period character-ized by fast and dynamic changes, are insufficient and may pro-vide an invaluable dimension to the decision making process. Inthis paper, a knowledge-based expert system (ES) is implementedto support the choice of the most suitable load forecasting model formedium/long term power system planning. In the proposed ES, thedetailed problem statement including forecasting algorithms andthe key variables (electrical and nonelectrical variables) that affectthe demand forecasts are firstly identified. A set of decision rulesrelating these variables are then obtained and stored in the knowl-edge base. Afterwards, the best model that will reflect accuratelythe typical system behavior over other models is suggested to pro-duce the annual load forecast. A practical application is given todemonstrate the usefulness of the developed prototype system.

Index TermsArtificial neural network (ANN), expert sys-tems (ES), fast/normal developing utility, forecasting methods,long-term load forecast.

I. INTRODUCTION

LONG-TERM load forecasting represents the first step indeveloping future generation, transmission, and distribu-tion facilities. Any substantial deviation in the forecast, partic-ularly under the new market structure, will result in either over-building of supply facilities, or curtailment of customer demand.The confidence levels associated with classical forecasting tech-niques [1], [2] when applied to forecasting problem in matureand stable utilities are unlikely to be similar to those of dynamicand fast growing utilities [3], [4]. This is attributed to the differ-ences in the nature of growth, socio-economics conditions, oc-currence of special events, extreme climatic conditions, and thecompetition in generation due to the deregulation of the elec-tricity sector with possible changes in tariff structures. Undersuch conditions, these forecasting techniques are insufficient toestablish demand forecast for medium/long term power systemplanning. Consequently, this case requires separate considera-tion either by pursuing the search for more improvement in theexisting forecasting techniques or establishing another approachto address the forecasting problem of such systems.

In this paper, the classical forecasting methods are firstlyapplied to obtain the long-term peak demand forecasts, for atypical fast growing utility as well as normal developing system.

Manuscript received March 8, 2000; revised December 4, 2001.M. S. Kandil is with the Electrical Engineering Department, Mansoura Uni-

versity, Mansoura, Egypt.S. M. El-Debeiky is with the Electrical Engineering Department, Ain Shams

University, Cairo, Egypt.N. E. Hasanien is with the Egyptian Electricity Authority, Studies and Re-

searches Department, Cairo, Egypt, and with the Saudi Electricity Company,Jeddah, Saudi Arabia.

Publisher Item Identifier S 0885-8950(02)03815-4.

A poor performance is observed when such methods are ap-plied to fast developing system, whereas most of these modelsare valid when used to produce the forecasts of the normaldeveloping system. Consequently, an extended logistic modelis developed to reflect the critical forecasting problem in fastgrowing areas. Although the developed model gives an accuratepeak demand forecast compared with the classical models, it ishardly difficult to dependent on single method for producingthe demand of such fast growing and dynamic system. Thisis because several important factors related to the cyclic anddynamic events that contribute significantly to the system peakis difficult to involve it into the existing forecasting models.Thus, there is a need to develop a computational tool whichallow one to store the knowledge associated with this problemalong with the mathematical models to support the choice ofthe most suitable load forecasting model, for medium/longterm power system planning. Therefore, the implementationof long-term forecasting strategies using a knowledge-basedexpert system (ES) is then presented in this paper. In the pro-posed expert system, key system variables which have majoreffects on system peak are identified based on past plannersexperiences. A set of decision rules relating these variables arethen established and stored in the knowledge base to select therecommended forecasting. A practical application is given todemonstrate the usefulness of the developed prototype system.

II. IDENTIFICATION OF THE FORECASTING PROBLEM

A common forecasting problem in most fast growing utilitiesis the short and insufficient database, unparalleled experienceof fast growth and unavailability of forecasting techniques ca-pable of addressing adequately this particular problem. More-over, there exists a unique dynamic pattern of the energy anddemand resulting from the interaction of cyclic social eventsoccurring periodically on lunar and seasonal temperature effectrelated to Gregorian calendar. The resulting system load char-acteristics are complex to analyze and difficult to predict.

The peak demand of the considered system, has grown up atan average annual rate in excess of 9% in the last ten years. Thesystem operates in an area where ambient temperatures can ex-ceed 48 C in summer season. The ambient temperature exhibitsa unique pattern every year. The system peak demand is partiallyinfluenced by the temperature, both its magnitude and time of itsoccurrence. In addition to the maximum temperatures, there areother cyclic and dynamic events that contribute significantly tothe system peak demand. The load of these events vary in magni-tude and time every Gregorian year and hence their contributionto the annual system peak demand is dynamic in nature. These

0885-8950/02$17.00 2002 IEEE

492 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 17, NO. 2, MAY 2002

Fig. 1. Mean absolute percent errors associated with the forecasted load of thefast and normal developing systems from 1994 to 1998.

Fig. 2. Monthly peak demands of the fast developing system from 1986 to1998.

cyclic variable loads are unlike the static temperature dependentloads, which occur during a fixed period every Gregorian year.

III. APPLICATION OF CLASSICAL FORECASTING METHODSThe peak demand forecast is obtained for a typical fast devel-

oping system and a normal developing system (Egyptian PowerSystem in this case) as well, by direct application of the clas-sical forecasting methods during the period 1981 to 1993. Fig. 1shows the mean absolute percent error (MAPE) of the load fore-cast for both systems compared with the actual demands duringthe period 1994 to 1998. Whereas most of the applied modelsare valid for the normal developing system, the large error as-sociated with the forecast of the fast developing system, for fiveyears only, concludes that there is a need to improve the ex-isting forecasting techniques to address the forecasting problemof such systems. Hence, an improved model of the critical fore-casting problem is explained in Section IV.

IV. IMPROVED MODEL OF SYSTEM DYNAMIC LOADCHARACTERISTICS

A composite load characteristic of the considered powersystem as shown in Fig. 2, is investigated.

Two main load components can be identified. The first com-ponent is an intrinsic static temperature-dependent load whilethe other is highly unstable dynamic load. The static load is thesummer air-conditioning demand. The peak of this load movesin harmony with the maximum temperature. The dynamic loadcomponent results from the load contribution of cyclic eventsrelated to the lunar calendar. One important event is the fastingritual of Ramadan. During this month, abrupt rise in power de-mand is customary in the region. The increase in the power de-mand is attributed to the simultaneous consumption of energy ofconsumers during certain periods. As all lunar months move rel-ative to the seasons, the load contribution of this ritual cyclicallymoves with respect to the temperature-dependent load. The in-

Fig. 3. Actual peak load versus fitted peak demand of the improved model forthe fast developing system from 1981 to 1993.

TABLE ICOMPARISON OF ACTUAL AND ESTIMATED DEMAND USING THE

PROPOSED MODEL

teraction of the two load components creates highly unstableload pattern.

A modified logistic model is suggested to represent the actualpeak demands recorded from 1981 to 1993 as

(1)And the dummy variable is given by

(2)where is the annual peak demand, MW, is a variablecomprising the effect of time, maximum temperature and Ra-madan effects on peak demand, is the time (years), and

are weighting factors representing the effect of Ramadanand maximum temperature on the peak demand, respectively,and , , , , and are constant coefficients.

The weighting factors of both and in the period 1981to 1993 are firstly produced. Then, estimated values of the pro-posed model parameters are then obtained using the nonlinearregression [5]. A comparison between actual and fitted peak de-mands is shown in Fig. 3.

Table I shows peak demand forecasts compared with recordeddemands during the period 19941998.

The relatively large error associated with 1994 is attributed tothe load contribution of the school examinations season, whichwas held, for the first time, during the summer. The proposedmodel demonstrates better forecasts compared with forecastsobtained by direct application of classical time-series models.However, this may not be sufficient, as the future load charac-teristics will be influenced by many unpredictable events. Suchevents are the opening and the closing of the schools, school ex-aminations, rehabilitation of old settlements, and introduction ofcompetition in generation environment with possible changes intariff structures.

The results conclude that it is hardly difficult to depend onthe applications of the classical methods which expect to pro-vide an invaluable dimension to the decision making process.

KANDIL et al.: LONG-TERM LOAD FORECASTING FOR FAST DEVELOPING UTILITY 493

Fig. 4. Structure of the expert system for long-term load forecast.

Therefore, computational method which allow one to store theknowledge based on heuristics and good sense rules alongsidethe incorporation of the mathematical forecasting methods, isessentially needed.

Although the artificial neural network (ANN) approach hasbeen successfully implemented for short-term load forecasting[7], there is a difficult to use it in this case where the weightsof many unpredictable variables are a priori unknown. Thus,a proposed method for combining an expert system with themathematical forecasting models is developed to represent theproblem as explained in the next section.

V. EXPERT SYSTEM STRUCTUREThe main components of the proposed expert system are

shown in Fig. 4.With the knowledge base at hand (rules and facts), an infer-

ence engine can be used to search through this knowledge baseaccording to the solution strategy. The detailed procedures ofthe solution strategy to ascertain the accuracy and credibility ofselecting forecasting method using the production rules in therule base is described in Section V-D. In addition to knowledgebase, inference engine, solution strategy, a user interface is alsodeveloped in the expert system to facilitate the navigation be-tween the proposed expert system and the user.

A. Knowledge BaseAll the knowledge obtained from the past experience, past

studies [1][6] has to be elicited appropriately using an ade-quate technique. We used the production rules representations.The data facts (static and dynamic) are included in the knowl-edge base. The data is represented as a set of variables in FACTSform, each of which define the value of a given variable as fol-lows.

The variables of the formulated problem can be grouped intoStatic and Dynamic Facts as follows:

Static Facts: This kind of knowledge is developed beforestarting the planning process. A sample of these facts is: systemconditions to identify the current situation of the system, i.e.,

mature, or under developing, isolated or interconnected withother system, etc.

Forecasting horizon to define the load forecasting period, i.e.,medium, or long term.

Load pattern to describe the load behavior (stable, ordynamic pattern, cyclic, or seasonal pattern, or combi-nation of all, time of system peak, load types, etc.)

Historical peak load to indicate annual and seasonalgrowth, coincidence factor, area peak, etc.

Historical energy to describe the information related tonumber of consumers of each sector, consumption rate,tariff rate, etc.

Major factors affecting the system peak, i.e., weathervariables, economic variables, demographic variables,special event, suppressed demand, bulk loads to beconnected into the network, coincidence factor of thesystem peak, etc.

The following example of load information shows how thesefacts are represented.> (deffacts System_Load(System_peak_load ?spl system_load_factor ?slfSystem_coincidence_factor ?scf ) )> (deffacts Areas_Load(area_a p_load ?x area_b p_load ?y area_c p_load?z))> (assert (System_Load ( ?spl ?slf ?scf)))> (assert (Areas_Load ( ?x ?y ?z)))

Dynamic Facts: These facts are developed and automat-ically updated during the inference process to represent theplanning attributes needed for evaluating a decision makingprocess. Samples of these facts include the following:

load and energy attribute for the estimated load andenergy forecast;

system losses attribute for the estimated system losses; error attribute related to the forecasting model.1) Design of Data Structure (Facts): In the developed ex-

pert system the facts are stored in objects. The object-orientedprogramming (OOP) is used because this structure enables theuse of the notion of hierarchical, inheritance, and data encap-sulation. Besides, the possibility of factoring a large programinto smaller units that can be modified independently and moreeasily [8], [9].

In an OOP, each object represents a physical object of thePeak load and energy demand. The objects are defined by aset of attributes-values, which contains all concrete knowledgeabout the particular object. The first step is to identify the rele-vant object classes of the power system situation and to specifytheir relation as shown in Fig. 5

In the next step, common characteristics are generalized tosuperclasses, and more specific classes are specialized in sub-classes. The results are a hierarchy of inheritance. The classesIsolLoad, BukLoad, and LodMang are generalized into their su-perclasses Load which in turn is a specialized of class ElectricalVariables.

2) Design of Rule Base: Rules are expressed as IFTHENstatement, as shown in the following.

IF (premise) THEN (conclusion)

494 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 17, NO. 2, MAY 2002

Fig. 5. Object model of demand forecasting of an electrical power system.

In a rule-based system, the domain knowledge is represented asa set of rules that are checked against a collection of facts orknowledge about the current situation. When the IF portion ofa rule is satisfied by the facts, the action specified by the THENportion is performed.

The rules base stores the called abstract knowledge, whichis composed basically of heuristics of power system demand.During the inference process, the inference engine tries to matchthe facts of the working memory with the knowledge containedin the rules premises, trying to deduce new facts or to fire actionsinto process. In this work, the production rules has a formalismof object: attribute pairs (refers to properties of an object) asshown in Table II.

The developed rules are divided into four groups of rules, foroptimizing the inference process as shown in the following.

1) Group 1: Forecasting objective.2) Group 2: Forecasting techniques.3) Group 3: Forecasting criteria.4) Group 4: Forecasting uncertainty factors.This divisions in-groups allow a more efficient use of infor-

mation within the entire knowledge base. Each group is com-posed by several rules. Here is an example of the implementedrules for selecting an appropriate forecasting technique.

Time series method_Trend Extrapolation(defrule Trend_Model(forecast_period ?T)(utility _developing ?mature ?fast ?normal)(system_behviour ?stable ?unstable) (system_affectedfactors ?defined ?undefined)(system_trend _pattern ?cyclic ?seasonal ?annual?all ?)(system_data ?recent ?minimal ?)())

TABLE IIPRODUCTION RULES

Fig. 6. Solution search strategy of the ES.

)

(if ( ?T 2 & 5 & normal & stable & defined &an-nual& resonable & minimal &) then(assert (time series method))else( ?T 2 & 5 & fast & unstable & )then(assert (.)))

B. Inference EngineThe inference engine is to link the rules given in the knowl-

edge base and the associated condition input by the user withdata stored in the database in order to solve the problem. Theshell adopted for building the expert system CLIPS [8] allowsthe use of production rules and facts pattern matching to solvethe problem. Also, this shell allows object-oriented program-ming using RULES and FACTS, RULES and OBJECTS tosolve the forecasting problem.

C. Solution StrategyThe procedures of the solution strategy is to ascertain the

accuracy, suitability, and credibility of selecting forecastingmethod using the production rules in the rule base. An outlineof the solution strategy of the ES is shown in Fig. 6.

Based on the condition, input by the user, of the forecastingobjective and the situation of the power system to be used, theengine emulates the initial state and the different system in-formation. Then it checks the available forecasting methods tomatch the forecasting objective. Afterwards, the procedure ini-tiates the load forecasting computation with the most appropri-ated forecasting algorithms using the integrated system data-base.

KANDIL et al.: LONG-TERM LOAD FORECASTING FOR FAST DEVELOPING UTILITY 495

Fig. 7. Portion of main forecasting dialog of the proposed ES applied toEgyptian system.

The engine takes the results of the forecasted peak load andthe produced errors to match the rule base of the forecasting cri-teria. If the output results satisfy the imposed constraints withoutviolations, the knowledge-based system terminates the analysisprocess successfully. When the inference engine finds that theimposed constraints such as selection of forecasting method arenot satisfied, it has to satisfy the goal using another forecastingmethod that satisfies the corresponding rules. Therefore, the en-gine searches for the rules that match the pattern of describingthe forecast method. When the interfacing is completed aftergenerating the derived fact using forward chaining, the resultinggoal facts are displayed to user and stored in a file for possibleusers request.

D. User InterfaceThe object of this module is to make the developed ES

friendly with its users. This object is achieved by designingsets of Menus and Windows to help the user in his navigationthrough the ES.

VI. IMPLEMENTATION OF THE DEVELOPED EXPERT SYSTEMTo demonstrate the effectiveness of the developed ES, two

cases are examined to produce the peak load forecasts for thetwo systems explained above in this paper. The designed mod-ules of the proposed ES are executed using Clips Shell, VisualBasic 5, and spreadsheet packages. All modules have been exe-cuted on a PC-Pentium II.

Case 1) Normal Developing Utility: The proposed ES em-ulates time series method to perform the demandforecast for the normal developing system as shownin the Forecasting dialog of Fig. 7. Due to the sta-bility of the system behavior the ES emulates thepolynomial trend model as shown in Fig. 8. in theForecasting dialog of Fig. 7. Due to the stability ofthe system behavior the ES emulates the polynomialtrend model as shown in Fig. 8. This model was pre-viously recommended by the classical method (seeFig. 1). Using this model, the actual and forecastedload from 1981 to 2008 is developed as shown inFig. 9.

Fig. 8. Time series method for forecasting the normal developing system.

Fig. 9. Actual and forecasted load of the normal developing system(19812001).

Fig. 10. Sector wise energy consumption models of the fast developingsystem.

Fig. 11. Actual and forecasted peak load of the fast developing system from1985 to 2008.

Case 2) Fast Developing Utility: Employing the similarprocess of case 1 using the fast developing systeminformation, simulation method is addressed as amore reliable one. The best models of the energyconsumption are produced as shown in Fig. 10.

The best model for each category is developed and thesummation of the energy demand of all sectors forms the totalsystem energy. The forecasted peak load over the same periodis then produced as shown in Fig. 11 using the forecastedenergy, losses and the forecasted load factor.

496 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 17, NO. 2, MAY 2002

Fig. 12. Energy demand and load forecast of each area for transmissionplanning.

TABLE IIISTATISTICAL ERRORS COMPARISON OF DIFFERENT FORECASTING

METHODS (19941998)

After the ES developing the forecast on the total systembasis, which is sufficient for generation planning, it producesthe load forecasting of each area, which is essentially neededfor transmission planning studies [10]. Thus, the ES through itsinference engine, reads the future load results and the heuristicknowledge stored in its knowledge base to develop the peakdemand of each area. A summary of the relevant data of theforecasted peak load of each area for 2001 appears in theprintout of Fig. 12.

VII. FORECASTING ACCURACY

The statistical error comparisons for all methods for the fastdeveloping system from 1994 to 1998 are shown in Table III.

This table indicates that the implementation of the long-termforecasting using the ES is very close to actual recorded valuesand the proposed ES is more appropriated to yield the bestsystem forecast.

VIII. CONCLUSIONSThe results of the classical forecasting methods concludes

that there is no single method, when applied to fast developingsystem, has shown a constant and stable performance over theforecasting period where as most of these models are valid whenapplied to the normal utilities. In this paper, a long-term loadforecasting is developed and classified according to the fore-casting problem using a knowledge-based expert system (ES).The proposed methodology is applied successfully to forecastyearly peak load for normal and fast developing power systems.The results are found to be very close to the actual loads and tobe more accurate than those obtained from direct application.Since the expert system is very flexible in updating the fore-casting methods and heuristic rules, it is expected that the devel-

oped expert system can serve as a valuable assistant to systemplanners in performing their annual load forecasting duties. Fi-nally, it can be expected to serve as a valuable assistant also fortraining purposes.

REFERENCES[1] H. L. Willis, Spatial Electric Load Forecasting. New York: Marcel

Dekker, 1996.[2] D. W. Bunn and E. D. Farmer, Comparative Models for Electrical Load

Forecasting. New York: Wiley, 1985.[3] N. E. Hasanien, An accurate model of weather sensitive for long-term

load forecasting for fast growing power system, in Proc. Second Re-gional Conf. CIGRE Committees Arab Countries, Amman, Jordan, May1997.

[4] E. H. Barakat and M. A. M. Eissa, Forecasting monthly peak demand infast growing electric utility using a composite multi-regression decom-position model, Proc. Inst. Elect. Eng. C, vol. 136, no. 1, pp. 3541,Jan. 1989.

[5] B. V. Stephen, Statistics for Engineering Problem Solving. Boston,MA: PWS, 1994.

[6] Energy and Electricity Demand Forecasting for Nuclear Power Plan-ning in Developing Countries. Vienna: IAEA, 1988.

[7] A. S. Al Fuhaid, M. A. El-Sayed, and M. S. Mahmoud, Cascaded ar-tificial neural networks for short-term load forecasting, IEEE Trans.Power Syst., vol. 12, pp. 15241529, Nov. 1997.

[8] J. C. Giarratano, CLIPS Expert Systems: NASA Software TechnologyBranch, 1993, vol. I & II.

[9] M. S. Kandil, S. M. El-Debeiky, and N. E. Hasanien, Rule-basedsystem for determining units locations of a developed generationexpansion plan for transmission planning, Proc. Inst. Elect. Eng. Gen.Transmiss., Distribut., vol. 147, no. 1, pp. 6268, Jan. 2000.

[10] H. L. Wills and H. N. Tram, Load forecasting for transmission plan-ning, IEEE Trans. Power Syst., vol. 103, pp. 561568, Mar. 1984.

M. S. Kandil was born in Damanhour, Egypt, in 1938. He received the Ph.D.degree from the University of Manchester Institute of Science and Technology,Manchester, U.K., in 1967.

He was Professor, Head of the Electrical Department, and Dean of the Facultyof Engineering of Mansoura University, Mansoura, Egypy, where he is now aProfessor. His technical activities include power system protection, operation,security, planning, load forecast, and solar energy, as well as applications ofartificial intelligence to power system problems. He is a consultant to a numberof sub-utilities in Egypt.

S. M. El-Debeiky (SM77) was born in Egypt, in1939. He received the Ph.D. degree from CairoUniversity, Cairo, Egypt, in 1970. He was Head ofElectrical Department of the Faculty of Engineeringof Ain Shams University, Egypt, where he isnow a Professor. His technical activities includehigh-voltage engineering, over-voltage studies,and power system planning and operation. He is aconsultant for the Egyptian Power System Company.He has written more than 75 papers in internationaljournals.

Dr. El-Debeiky is a member of IEE and CIGRE.

N. E. Hasanien was born in Dyarb Negm, Egypt, in1957. He received the Ph.D. degree from Ain ShamsUniversity in 2001.

He was with the Egyptian Electricity Authorityas a system studies and planning engineer since1982. He has also been with the Saudi ElectricityCompany, Jeddah, Saudi Arabia, since 1989, wherehe is now a system planner. His technical activi-ties include system analysis, system generation,transmission planning, load forecast, switching andover-voltage studies, and applications of artificial

intelligence to power system problems.

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