International Journal of Computational
Intelligence Research (IJCIR)
Volume 3, Number 1 (2007)
A new approach for the short-term load forecasting with autoregressive and
artificial neural network models
Ummuhan Basaran Filik, Mehmet Kurban
Anadolu University, Department of Electric Engineering, Eskisehir, Turkey
In this paper, a new approach to the short-term load forecasting using autoregressive (AR) and artificial neural network (ANN) models is introduced and applied to the power system of Turkey by using the consumption values of electrical energy for three months in 2002, including January, February, and March. The load forecasting for the next day using AR and ANN models is performed separately, and the results of the AR analysis is used for the input of different ANN models, which are Feed Forward Back Propagation and Cascade Forward Back Propagation Models. The performance of these models was compared with each other. When the energy consumption is examined for the whole week, it was observed that Sundays are different from other six days in the weeks. Because of this, the values for the past six days except Sunday are used for the load forecasting of the next day. For the system that uses ANN models only, the network is composed of a 6 neuron input layer and a 1-neuron output layer; for the systems that use AR and ANN models, there are 7 neurons in the input layer and one neuron in the output layer. It was found that systems that use both AR and ANN models can achieve a higher forecasting accuracy.