Application of neural networks in short-term load forecasting

Authors:
Mohsen Hayati-Behnam Karami
Affiliations:
Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
Venue:
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
Year:
2005

Citing 3
Cited 0

Neural networks: algorithms, applications, and programming techniques

Neural networks: algorithms, applications, and programming techniques
Artificial Neural Networks: A Tutorial

Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Recurrent Neural Networks: Design and Applications

Recurrent Neural Networks: Design and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Artificial neural network is a computational intelligence technique that has found major applications in engineering and science. One of them is to design short-term load forecasting systems (STLF) which due to its complicated and nonlinear nature, the study of these systems requires an efficient computational tool which neural networks can do it well. In this paper we explore the use of neural networks to study the design of STLF Systems. We use the three important architectures of neural networks named Multi Layer Perceptron (MLP), Elman Recurrent Neural Network (ERNN) and Radial Basis Function Network (RBFN) to model STLF systems. The results show that RBFN networks have the minimum forecasting error and are the best method to model the STLF systems.