Artificial neural networks in short term load forecasting / Zulkifle Ahmad

Ahmad, Zulkifle (2003) Artificial neural networks in short term load forecasting / Zulkifle Ahmad. Degree thesis, Universiti Teknologi MARA (UiTM).

Abstract

The artificial neuron network (ANN) technique for short-term load forecasting (STLF) has been proposed by several authors in order to evaluate ANN as a viable technique for STLF. Once we have to evaluate the performance of ANN methodology for practical considerations of STLF problem. This paper wills presents the results of a study to look the effectiveness of next 1 hour ANN model in 24-hour load profile at the one time was compared with the previous load on 3 months load. Data from utilities were used in modeling and forecasting. In this thesis, the back propagation will be applied as the most popular technique in Artificial Neural Network. This model is propagated forward and the error between the actual and desired output is back propagated to obtain a minimize error closer to zero. From this study we can also find that whether the Short Term Load Forecasting, Artificial Neural Network is sensitive load forecast or not.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Ahmad, Zulkifle
UNSPECIFIED
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Abd. Hadi, Razali
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunication > Computer networks. General works. Traffic monitoring
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Bachelor of Electrical Engineering (Hons)
Keywords: Artificial neuron network, short-term, load forecast
Date: 2003
URI: https://ir.uitm.edu.my/id/eprint/67083
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