costs are considered taking into account the constraints of conductorcapacities and voltage drop. The investment costs will take into accountthat some cables can be lying in the same trench. The process was ap-plied for a Spanish city of 200,000 inhabitants.

Keywords: Network design, medium-voltage network planning,urban distribution network, evolutionary algorithm.

Preprint Order Number: PE-134PRS (04-2002)Discussion Deadline: September 2002

Generation Expansion Planning:An Iterative Genetic Algorithm Approach

Kazay, H.E; Legey, L.F.L.

Author Affiliation: Brazilian Electric Power Research Center,Brazil; Federal University of Rio De Janeiro, Brazil

Abstract: The generation expansion planning problem (GEP) is alarge-scale stochastic nonlinear optimization problem. To handle theproblem complexity, decomposition schemes have been used. Usually,such schemes divide the expansion problem into two subproblems: onerelated to the construction of new plants (investment subproblem) andanother dealing with the task of operating the system (operationsubproblem). This paper proposes an iterative genetic algorithm (IGA)to solve the investment subproblem. The basic idea is to use a specialtype of chromosome, christened the pointer-based chromosome (PBC),and the particular structure of that subproblem, to transform an inte-ger-constrained problem into an unconstrained one. IGA's results werecompared to those of a branch and bound algorithm (provided by acommercial package) in three different case studles of growing com-plexity, respectively containing 144, 462, and 1845 decision variables.These results indicate that the IGA is an effective altemative to the solu-tion of the investment subproblem.

Keywords: Genetic algoritluns, integer programming optimizationmethods, planning, power systems, uncertainty.

Preprint Order Number: PE-249PRS (04-2002)Discussion Deadline: September 2002

Artificial Neural Network-Based Peak LoadForecasting Using Conjugate Gradient Methods

Saini, L.M.; Soni, M.K.

Author Affiliation: Regional Engineering College, IndiaAbstract: Daily electrical peak load forecasting has been done us-

ing the feed forward neural network based upon the conjugate gradientback propagation methods by incorporating the effect of elevenweather parameters, the previous day's peak load information, and thetype of day. To avoid the trapping of the network into a state of localminima, the optimization of user-defined parameters viz., leaming rateand error goal has been performed. The training data-set has been se-lected using a growing window concept and is reduced per the nature ofthe day and the season for which the forecast is made. For redundancyremoval in the input variables, reduction of the number of input vari-ables has been done using the principal component analysis method offactor extraction. The resultant data set is used for the training of athree-layered neural network To increase the leaming speed, theweights and biases are initialized according to the Nguyen and Widrowmethod. To avoid overfitting, an early training is stopped early at theminimum validation error.

Keywords: Back propagation, gradient methods, load forecasting,neural networks.

Preprint Order Number: PE-255PRS (04-2002)Discussion Deadline: September 2002

A Heuristic Meter PlacementMethod for Load Estimation

Yu, D.C.; Liu, H.; Chiang, H.D.

Author Affiliation: Cooper Power Systems; University of Wiscon-sin-Milwaukee; Comell University, Ithaca, NY

Abstract: A heuristic method of optimal meter placement for loadestimation in distribution systems is presented in this paper. The ap-proach can be used to efficiently find the meter location candidates forload estimation. The meter placement method presented in this paperhas a two-stage approach. In the first stage, meters are placed using aheuristic method. In the second stage, the confidence interval is calcu-lated to determine if the meters give satisfactory results when loadsvary between the maximum and minimum. Sample system analysis andtesting results show the approach is efficient for finding tentative meterlocations. Real application constraints such as meter failure backup,availability of space, automated switch locations, and unbalanced sys-tems are also considered. The meter placement method for load estima-tion can be easily extended to place meters for circuit state estimation.

Keywords: Power distribution planning, heuristic method, loadestimation, meter placement.

Preprint Order Number: PE-337PRS (04-2002)Discussion Deadline: September 2002

Incorporating Aging Failures inPower System Reliability Evaluation

Li, W

Author Affiliation: BC Hydro, CanadaAbstract: This paper presents a method for incorporating aging

failures in power system reliability evaluation. It includes developmentof a calculation approach with two possible probability distributionmodels for unavailability of aging failures and implementation in reli-ability evaluation. The defined unavailability of aging failures has aconsistent form as that for repairable failure. This allows aging failuresto be easily included in existing reliability evaluation techniques andtools. Differences between the two models using normal and Weibulldistributions have been discussed. The BC Hydro north metro systemwas used as an example to demonstrate an application of the proposedmethod and models. The results indicate that aging failures have signif-icant impacts on system reliability, particularly for an "aged" system.Ignoring aging failures in reliability evaluation of an aged power sys-tem will result in an overly underestimation of system risk and mostlikely a misleading conclusion in system planning.

Keywords: repairable failure, aging failure, aged system, powersystem reliability, unavailability.

Preprint Order Number: PE-414PRS (04-2002)Discussion Deadline: September 2002

Neural Network Load Forecastingwith Weather Ensemble Predictions

Taylor, J.W; Buizza, R.

Author Affiliation: University of Oxford, Oxford, U.K.; EuropeanCenter for Medium-Rang Weather Forecasts, Reading, U.K.

Abstract: In recent years, a large literature has evolved on the use ofartificial neural networks (NNs) for electric load forecasting. NNs areparticularly appealing because of their ability to model an unspecifiednon-linear relationship between load and weather variables. Weatherforecasts are a key input when the NN is used for forecasting. Thisstudy Investigates the use of weather ensemble predictions in the appli-cation of NNs to load forecasting for lead times from 1 to 10 daysahead. A weather ensemble prediction consists of multiple scenariosfor a weather variable. We use these scenarios to produce multiple sce-narios for load. The results show that the average of the load scenariosis a more accurate load forecast than that produced using traditionalweather forecasts. We use the load scenarios to estimate the uncertaintyin the NN load forecast This compares favourably with estimates basedsolely on historical load forecast errors.

Keywords: Load forecasting; neural networks weather ensemblepredictions.

Preprint Order Number: PE-567PRS (04-2002)Discussion Deadline: September 2002

IEEE Power Engineering Review, July 2002 59