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Article
Estimation Load Forecasting Based on the Intelligent Systems

Authors: Hanan A.R. Akkar --- Wissam H. Ali
Journal: AL-NAHRAIN JOURNAL FOR ENGINEERING SCIENCES مجلة النهرين للعلوم الهندسية ISSN: 25219154 / eISSN 25219162 Year: 2018 Volume: 21 Issue: 2 Pages: 285-291
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

The daily peak load forecasting for the next day is the basic operation of generation scheduling. The approach of using ANN methodology alone is limited which has generated interest to explore hybrid system. In this paper a search of genetic programming to a short term load forecasting is presented. A genetic architecture with the fitness normalization has been used to find as optimum data peak load of Baghdad city. The optimize data applied to the ANN to be trained and tested to estimate the daily peak load of Baghdad city. Different cases for load forecasting are considered with the aid of MATLAB 7 package to get the estimation of the next day. So an improvement method of genetic optimization is proposed to get a better solution for the load estimation rather than artificial neural network.


Article
Short Term Load Forecasting Based Artificial Neural Network

Author: Adel M. Dakhil
Journal: Iraqi Journal for Electrical And Electronic Engineering المجلة العراقية للهندسة الكهربائية والالكترونية ISSN: 18145892 Year: 2014 Volume: 10 Issue: 1 Pages: 42-47
Publisher: Basrah University جامعة البصرة

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Abstract

Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday

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