Short term electricity load forecasting using Artificial Neural Network (ANN): article

Mohd Zuki, Muhammad Zakwan (2014) Short term electricity load forecasting using Artificial Neural Network (ANN): article. pp. 1-6.

Abstract

This paper has presents a study of electricity load forecasting demand by using artificial neural network (ANN). Generally, there are three levels of processing forecasting data using artificial neural network (ANN) which are input layer, hidden layer and output layer. This method was developed using MATLAB software which produced the accurate result of this load forecasting. Mean Absolute Percentage Error (MAPE) was applied to show the differences between predicted value and actual load data. The study forecasts the amount of consumed in the next 24-hours. Table of historical hourly loads of DUKE, USA from 26th March 2012 until 4th July 2012 was used in this paper. Forecasting load demand is very important for the operation of generating electricity supply companies because it helps to make decisions to generate enough power electric to consumer as well as to control operation of electric usage of the company’s infrastructure.

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Item Type: Article
Creators:
Creators
Email / ID Num.
Mohd Zuki, Muhammad Zakwan
mrzakwan08@gmail.com
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Applications of electric power
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Applications of electronics
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Page Range: pp. 1-6
Keywords: Component, Load forecasting, Artificial neural network (ANN), Electric power, MAPE
Date: July 2014
URI: https://ir.uitm.edu.my/id/eprint/114874
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