Electricity Forecasting for Small Scale Power System Using Artificial Neural Network

Khamis, M.F.I. and Baharudin , Z. and Hamid, N.H. and Abdullah, M. F. and Solahuddin, S. (2011) Electricity Forecasting for Small Scale Power System Using Artificial Neural Network. In: Fifth International Power Engineering and Optimization Conference (PEOCO2011), 6th - 7th June 2011, Shah Alam, Selangor Malaysia.

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Short term load forecasting (STLF) method is the
basis of efficient operation for power system. It has an important
role in planning and operation of power system. In this paper, a
practical STLF using artificial neural network method (ANN) for
Gas District Cooling (GDC) power plant of Universiti Teknologi
PETRONAS (UTP) is presented. As a sole customer of GDC
power plant, the load data from 2006 till 2009 are gathered and
utilized for model developments. The developed models forecast
electricity load for one week ahead. The paper proposes a method
of a multilayer perceptron neural network and it is trained and
simulated by using MATLAB. The models have been tested with
the actual load data and perform relatively good results.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE: Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Sustainable Building
Depositing User: Dr Zuhairi Baharudin
Date Deposited: 21 Nov 2011 06:29
Last Modified: 19 Jan 2017 08:22
URI: http://scholars.utp.edu.my/id/eprint/6743

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