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Electricity Forecasting For The Small Scale Power System Using Artificial Neural Network

NOORAZLIZA BINTI SULAIMAN, NOORAZLIZA (2010) Electricity Forecasting For The Small Scale Power System Using Artificial Neural Network. Universiti Teknologi Petronas, Sri Iskandar,Tronoh,Perak. (Unpublished)

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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. Model 1 is developed to forecast for one week ahead for Semester OFF. Model 2 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
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 5 not found.
Date Deposited: 03 Nov 2011 11:23
Last Modified: 25 Jan 2017 09:43
URI: http://utpedia.utp.edu.my/id/eprint/1142

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