Forecast on electricity consumption in Malaysia by using artificial neural network (ANN) / Mohd Syahmin Mohamed Othman

Mohamed Othman, Mohd Syahmin (2010) Forecast on electricity consumption in Malaysia by using artificial neural network (ANN) / Mohd Syahmin Mohamed Othman. Degree thesis, Universiti Teknologi MARA (UiTM).

Abstract

Forecast of electricity consumption is very important for both suppliers and large consumers. However, the electricity consumption of a large enterprise is quite different with regional consumption, and has not been studied sufficiently, especially for an energy intensive corporation. An essential element of electric utility resource planning is forecasting the electricity consumption for the long term. This study presents an approach to forecast the annual electricity consumption based on historical data for Malaysia by using artificial neural network (ANN). The project involves developing several ANN designs to develop network and testing that network appropriately. ANN with its best performance was selected as the best design. After obtaining the most reliable model, forecasting the electricity consumption by using ANN is performed. The network is developed by means of economical conditions and how the variables are going to be changed in the following years. The model of network yields gives a very satisfactory results and the range of electricity consumption is obtained. Consequently, forecasting the electricity consumption in Malaysia can be successfully done.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Mohamed Othman, Mohd Syahmin
2007270486
Contributors:
Contribution
Name
Email / ID Num.
Advisor
Johari, Dalina
UNSPECIFIED
Subjects: C Auxiliary Sciences of History > CB History of civilization > Forecasts of future progress
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Bachelor in Electrical Engineering (Hons.)
Keywords: Electricity, artificial neural network, energy
Date: 2010
URI: https://ir.uitm.edu.my/id/eprint/79529
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