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Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network

[ Vol. 13 , Issue. 7 ]

Author(s):

Muhammad Faizan Tahir, Chen Haoyong*, Kashif Mehmood, Noman Ali Larik, Asad Khan and Muhammad Sufyan Javed   Pages 980 - 992 ( 13 )

Abstract:


Short Term Load Forecasting (STLF) can predict load from several minutes to week plays a vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, make this algorithm unable to accurately predict future loads. This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

Keywords:

Short term load forecasting, artificial neural network, multi-layer perceptron, bootstrap aggregating, disjoint partition, ensemble artificial neural network.

Affiliation:

School of Electric Power, South China University of Technology, Guangzhou, School of Electric Power, South China University of Technology, Guangzhou, School of Electrical Engineering, Southeast University, Nanjing China, School of Electric Power, South China University of Technology, Guangzhou, School of Computer Science, South China Normal University, Guangzhou, Department of Physics, Jinan University, Guangzhou



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