A new estimation algorithm for electric load forecast model identification

This paper presents a new approach for estimating parameters of long term load forecast models. The objective is to reduce the total estimation error by appropriately adjusting the model coefficients. The proposed method is based on particle swarm optimization algorithm that has been getting added attention as a powerful optimization tool in recent years. It is developed to minimize the error associated with the estimated model parameters. Both linear and nonlinear forecast models have been used to perform this study. Actual reported data of Kuwait network is used to analyze the performance of the proposed approach. Results are reported and compared to those obtained using different estimation techniques. Comparison results are in favor of the proposed approach which signifies its potential as a promising estimation tool.

[1]  A. D. Patton,et al.  Development of an intelligent long-term electric load forecasting system , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[2]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  J. K. Mandal,et al.  Hierarchical dynamic state estimator using ANN-based dynamic load prediction , 1999 .

[4]  M. El-Hawary,et al.  Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation , 1993 .

[5]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[6]  Mohamed E. El-Hawary,et al.  A Survey of Particle Swarm Optimization Applications in Electric Power Systems , 2009, IEEE Transactions on Evolutionary Computation.

[7]  K. M. El-Naggar,et al.  Electric Load Forecasting Using Genetic Based Algorithm, Optimal Filter Estimator and Least Error Squares Technique: Comparative Study , 2007 .

[8]  W. M. Grady,et al.  Enhancement, implementation, and performance of an adaptive short-term load forecasting algorithm , 1991 .

[9]  H. Lee Willis,et al.  Comparison Tests of Fourteen Distribution Load Forecasting Methods , 1984, IEEE Power Engineering Review.

[10]  G. E. Huck,et al.  Load Forecast Bibliography Phase I , 1980, IEEE Transactions on Power Apparatus and Systems.

[11]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[12]  S. M. El-Debeiky,et al.  Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System , 2002, IEEE Power Engineering Review.

[13]  A. C. Liew,et al.  Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting , 1995 .