Short-Term Load Forecasting Based on GA-Elman Model

Wen Xia You; Jun Xiao Chang; Zi Heng Zhou; Ji Lu

doi:10.4028/www.scientific.net/AMR.986-987.520

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 986-987Short-Term Load Forecasting Based on GA-Elman...

Short-Term Load Forecasting Based on GA-Elman Model

Abstract:

Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

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Periodical:

Advanced Materials Research (Volumes 986-987)

Pages:

520-523

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.986-987.520

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Online since:

July 2014

Authors:

Wen Xia You*, Jun Xiao Chang, Zi Heng Zhou, Ji Lu

Keywords:

Error, GA-Elman, Genetic Algorithm (GA), Load Forecasting, Neural Network (NN)

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* - Corresponding Author

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