an artificial neural networks forecasting for malaysia’s load

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ID: 256816
2014
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Abstract
In this paper, two artificial neural networks models, namely the multilayer feedforward neural network and the recurrent neural network are applied for Malaysia's load forecasting. A half hourly load data is divided equally into three distinct sets for training, validation and testing. Backpropagation is selected as the learning algorithm whereas the transfer function for both hidden layer and output layer is sigmoid the function. The forecasting performances were compared between these two models. The results show that, the sum squared error (SSE) of multilayer feedforward neural network were the lowest hence the multilayer feedforward neural network is a better model for a half hourly Malaysia's load.
Reference Key
mohamed2014statistikaan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Norizan Mohamed;Maizah Hura Ahmad;Zuhaimy Ismail;Khairil Anuar Arshad
Journal palgrave communications
Year 2014
DOI
DOI not found
URL
http://ejournal.unisba.ac.id/index.php/statistika/article/view/985
Keywords
statistics

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