Subscribe Now Subscribe Today
Science Alert
 
Blue
   
Curve Top
Journal of Applied Sciences
  Year: 2007 | Volume: 7 | Issue: 12 | Page No.: 1582-1588
DOI: 10.3923/jas.2007.1582.1588
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Short Term Load Forecasting Using Artificial Neural Networks for the West of Iran

Mohsen Hayati

Abstract:
In this study, the use of neural networks to study the design of Short-Term Load Forecasting (STLF) Systems for the west of Iran was explored. 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 were used. The results show that RBFN networks have the minimum forecasting error and are the best method to model the STLF systems.
PDF Fulltext XML References Citation Report Citation
 RELATED ARTICLES:
  •    Optimizing Humidity Level and Illuminance to Enhance Worker Performance in an Automotive Industry
  •    Long-term Load Forecasting in Power System: Grey System Prediction-based Models
  •    Image Reconstruction Algorithm for Electrical Capacitance Tomography
How to cite this article:

Mohsen Hayati , 2007. Short Term Load Forecasting Using Artificial Neural Networks for the West of Iran. Journal of Applied Sciences, 7: 1582-1588.

DOI: 10.3923/jas.2007.1582.1588

URL: https://scialert.net/abstract/?doi=jas.2007.1582.1588

COMMENT ON THIS PAPER
 
 
 

 

 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 

Curve Bottom