• Corpus ID: 6390557

Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region

  title={Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region},
  author={Mohsen Hayati and Yazdan Shirvany},
  journal={International Journal of Electrical and Computer Engineering},
  • M. Hayati, Y. Shirvany
  • Published 24 April 2007
  • Computer Science
  • International Journal of Electrical and Computer Engineering
Abstract— In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF… 

Figures and Tables from this paper

This paper focuses on short term load forecasting by using a hybrid model of neural networks and fuzzy logic, and reveals that the drawbacks pertaining to these approaches seem to be complementary and IJEEERD.
Short-Term Load Forecasting Using Artificial Neural Networks
An accurate regional load forecasting is very important in improving management performance of Power Plant Generation. Various regional load forecasting methods have been developed for 24 hours
Short-Term Load Forecasting using Artificial Neural Network Techniques
Artificial Neural Network (ANN) Method is applied to fore cast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day
Short term load forecasting of Indian system using linear regression and artificial neural network
The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly
Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis
Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System models were used to analyse data collection obtained from the Metrological Department of Malaysia for long-term load forecasting and showed that the results for ANFIS produced much more accurate results compared to ANN.
Short Term Load Forecasting Of Urban Loads Based On Artificial Neural Network
The performance of the neural network is tested through an extensive study of the hourly-based data from the Roorkee region and the obtained results convey the sustainability /suitability of the methodology used in the paper for short-term load forecasting.
Load Forecasting Using Multi-Layer Perceptron Neural Network
Load forecasting has become one of the major areas of research in electrical engineering and is an important problem in operation and planning of electric power generation. Load forecasting is the
Next day electric load forecasting using Artificial Neural Networks
The use of Artificial Neural Networks (ANN) by power distribution companies has gained a wide reception due to its ability to predict close to accurate forecasted electric load consumption. A local
Short term load forecasting using artificial neural network
Load forecasting has been done using ANN (Artificial Neural Network) for short term load forecasting of NEPOOL region of ISO New England using neural network toolbox with 20 neurons.


One-Hour-Ahead Load Forecasting Using Neural Networks
Load forecasting has always been an essential part of an efficient power system's planning and operation. Several electric power companies are now forecasting load power based on conventional
Analysis and Evaluation of Five Short-Term Load Forecasting Techniques
A comparative evaluation of five short-term load forecasting techniques is presented and the transfer function (TF) approach gave the best result, whereas for the peak winter day the TF approach resulted in the next to the worst accuracy.
Neural Network Load Forecasting with Weather Ensemble Predictions
In recent years, a large literature has evolved on the use of artificial neural networks (NNs) for electric load forecasting. NNs are particularly appealing because of their ability to model an
Short-term load forecasting via ARMA model identification including non-Gaussian process considerations
The concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process considerations, and with embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately.
Neural network design
This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Introduction to artificial neural systems
Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.
Neural Networks: A Comprehensive Foundation
Simon Haykin Neural Networks A Comprehensive Foundation Simon S. Haykin neural networks a comprehensive foundation pdf PDF Drive.