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 The Small Scale Power System Using Artificial Neural Network

Sulaiman, Noorazliza (2010) Electricity Forecasting For The Small Scale Power System Using Artificial Neural Network. Universiti Teknologi Petronas. (Unpublished)

[img] PDF
Download (2006Kb)

Abstract

The study is about to forecast the electricity demand values of UTP. The electricity profile of GDC (UTP) has been analyzed based on the historical data gathered. Using the analyzed data, forecast models have been developed prior to do forecasting. The models are being developed using Artificial Neural Network method. There are four models have been developed based on the conditions in UTP. Modell is developed to forecast for one week ahead for Semester OFF. Model2 is developed to forecast for one week ahead for Semester ON. Furthermore, Model 3 and 4 are developed to forecast for 30 days ahead for Semester OFF and ON respectively. Upon developed the robust models, all the models have been simulated using five (5) different hidden neurons. As to obtained accurate forecasting result, the models have been simulated for twenty simulations for each of the hidden layer. From that, the error between forecasted and actual load have been obtained. From the result of the error calculation, the best forecast model is being chosen. Upon completing the project, the conclusion has been made based on the result from the forecasting as well as the values of MAPE.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Embedded Systems
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 22 Oct 2013 11:56
Last Modified: 25 Jan 2017 09:42
URI: http://utpedia.utp.edu.my/id/eprint/9366

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...