Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim

Abd Rahim, Muhamad Farhan (2013) Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim. Degree thesis, Universiti Teknologi MARA (UiTM).

Abstract

This project focuses on forecast of electricity consumption in Malaysia using artificial intelligence. From the world market, electricity consumption depends on the electrical usage of a bunch of society. Electricity consumption should correspond to the current demand because the production of excess electricity and the reduction of electricity can cause economic loss. Almost of the large scale, it is impossible to do complete inspection because the time and cost increases drastically with increase in number of samples. This has created a need for a system that can inspect the components automatically with less cost and less time. The ANN will generate the pattern and predict the future pattern of electricity consumption. To improve the result of ANN model, the optimization method was used to optimize the forecast. As the result, the range of electricity consumption is obtained.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Abd Rahim, Muhamad Farhan
UNSPECIFIED
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Johari, Dalina
UNSPECIFIED
Subjects: Q Science > Q Science (General) > Back propagation (Artificial intelligence)
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Bachelor of Electrical Engineering (Hons.)
Related URLs:
Keywords: Electricity consumption, artificial intelligence, excess electricity
Date: 2013
URI: https://ir.uitm.edu.my/id/eprint/84794
Edit Item
Edit Item

Download

[thumbnail of 84794.pdf] Text
84794.pdf

Download (4MB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:
On Shelf

ID Number

84794

Indexing

Statistic

Statistic details