Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

SOLAHUDDIN, SALWA (2011) ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK. Universiti Teknologi Petronas. (Unpublished)

[img] PDF
Download (3130Kb)

Abstract

This project presents a practical short term load forecasting (STLF) for small scale power system using artificial neural network (ANN) method. The project applies a generic three-layered feedforward network. The network is trained in a supervised manner and used backpropagation as a learning teclmique. In addition, a configuration consisting of a hidden layer that uses a hyperbolic tangent sigmoid transfer function and the output layer with a pure linear transfer function is applied. Gas District Cooling (GDC) is chosen as a case study for small scale power system since this plant was designed to produce electrical power supply and chilled water for Universiti Teknologi PETRONAS (UTP) campus and in-plant use. As a sole customer of GDC power plant, the load data from 2006 till 20 I 0 are gathered and utilized for model developments. There are two models developed based on UTP normal operating semester (Semester On) and during break (Semester Off). The developed models can forecast electricity load for the one week ahead. The computation experimental of the proposed network applies MATLAB software and its toolbox. The mean absolute percentage error (MAPE) is used as the measurement for the forecasting performance. At the end of this project, the proposed method using ANN manages to get average MAPE of 6.72 % for Model I (Semester Off) and 3.92 % for Model 2 (Semester On) which is considered relatively good result.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Power Systems - Distribution - Power distribution
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 30 Sep 2013 16:49
Last Modified: 25 Jan 2017 09:42
URI: http://utpedia.utp.edu.my/id/eprint/7324

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...