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A Combined Model Based on Neural Networks, LSSVM and Weight Coefficients Optimization for Short-Term Electric Load Forecasting

  • Caihong LiEmail author
  • Zhaoshuang He
  • Yachen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

As an essential energy in the daily life, electricity which is difficult to store has become a hot issue in power system. Short-term electric load forecasting (STLF) which is regarded as a vital tool helps electric power companies make good decisions. It can not only guarantee adequate energy supply but also avoid unnecessary wastes. Although there exists quantity of forecasting methods, most of them are not able to make accurate predictions. Therefore, a forecasting method with high accuracy is particularly important. In this paper, a combined model based on neural networks and least squares support vector machine (LSSVM) is proposed to improve the forecasting accuracy. At first, three forecasting methods named generalized regression neural network (GRNN), Elman, LSSVM are utilized to forecast respectively. Among them, simulate anneal (SA) arithmetic is used to optimize GRNN. Then, SA is employed to determine the weight coefficients of each individual method. At last, multiplying all the three forecasting results with the corresponding weights, the final result of the combined model can be attained. Using the electric load data of Queensland of Australia as experimental simulation, case studies show that the proposed combined model works well for STLF and the results prove more accurate.

Keywords

Generalized regression neural network Elman Least squares support vector machine simulated annealing algorithm Short-term electric load forecasting 

Notes

Acknowledgments

The authors would like to thank the Natural Science Foundation of PR of China (61073193,61300230), the Key Science and Technology Foundation of Gansu Province (1102FKDA010), the Natural Science Foundation of Gansu Province (1107RJZA188), and the Science and Technology Support Program of Gansu Province (1104GKCA037) for supporting this research.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouPeople’s Republic of China

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