An Artificial Neural Networks Forecasting for Malaysia’s Load - Norizan Mohamed; Maizah Hura Ahmad; Zuhaimy Ismail; Khairil Anuar Arshad (STATISTIKA: Forum Teori dan Aplikasi Statistika, 2008)
In this paper, two artificial neural networks models, namely the multilayer feedforward neuralnetwork and the recurrent neural network are applied for Malaysia's load forecasting. A half hourlyload 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 hiddenlayer and output layer is sigmoid the function. The forecasting performances were compared betweenthese two models. The results show that, the sum squared error (SSE) of multilayer feedforwardneural network were the lowest hence the multilayer feedforward neural network is a better model fora half hourly Malaysia's load.