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NEURAL NETWORK APPLICATION TO SHORT TERM LOAD FORECAST

MARZUKI, MOHAMAD FAIZ BIN (2012) NEURAL NETWORK APPLICATION TO SHORT TERM LOAD FORECAST. Universiti Teknologi Petronas.

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Abstract

Power system planning and operation is an important part for power systems industry. By having a good planning and operation, the quality of power supplied will be improved ensuring both consumer and power provider getting their share equally. In this case, the most challenging part is the prediction of how much the load that will be used by the consumer for a short period of time. This prediction is called load forecasting. This will be very useful to every power system company as the trending for the load demand is different for each geographical location. There are different methods to do the load forecasting. One of the project involved MATLAB program for the short term load forecasting (STLF) using Artificial Neural Network (ANN) model. We are using Multilayer Perceptron (MLP) Neural Network architecture, it will improve the forecast value significantly by obtain a very small mean absolute percentage error (MAPE). By getting a smaller MAPE, it represents higher forecast accuracy of the model itself. The elements in this report contain of an introduction, problem statement, objectives, literature review and methodology which was used to solve the forecasting problems. The discussion of the obtained results will be looked further in this project.

Item Type: Final Year Project
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 1278 not found.
Date Deposited: 05 Oct 2012 10:01
Last Modified: 25 Jan 2017 09:40
URI: http://utpedia.utp.edu.my/id/eprint/3941

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