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ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

ABD. MALIK, MUHAMMAD IZZAT (2011) ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK. Universiti Teknologi PETRONAS. (Unpublished)

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Abstract

Short-term load forecast is an essential part of electric power system planning and operation. For this project, the main focus will be on the Gas District Cooling Plant (GDC) which acts as the primary source of energy for Universiti Teknologi PETRONAS (UTP). This project is looking into weekly forecast of the electricity production for the GDC plant using Artificial Neural Network Approach. This forecasting method will be very useful to support plant operation as the trending of load demand for an educational centre such as UTP is very much dependent on the university activities itself. The project involve MATLAB program for the STLF with Artificial Neural Network prediction model. The obtained results showed that introducing Multilayer Perceptron (MLP) Neural Network architecture improve the prediction significantly by obtaining a very small value of Mean Absolute Percent Error (MAPE). Besides that, by getting the smaller value of MAPE, it represents higher forecast accuracy of the model itself. The report consists of an introduction, problem statement, objectives, literature review and methodology used to solve the problem. It further looks into the obtained results with consistent discussion.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Test and Reliablity
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 12 Nov 2013 08:55
Last Modified: 25 Jan 2017 09:41
URI: http://utpedia.utp.edu.my/id/eprint/10444

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